- abort(Throwable) - 接口 中的方法org.apache.spark.shuffle.api.ShuffleMapOutputWriter
-
- abort(WriterCommitMessage[]) - 接口 中的方法org.apache.spark.sql.connector.write.BatchWrite
-
- abort() - 接口 中的方法org.apache.spark.sql.connector.write.DataWriter
-
Aborts this writer if it is failed.
- abort(long, WriterCommitMessage[]) - 接口 中的方法org.apache.spark.sql.connector.write.streaming.StreamingWrite
-
- abortJob(JobContext) - 类 中的方法org.apache.spark.internal.io.FileCommitProtocol
-
Aborts a job after the writes fail.
- abortJob(JobContext) - 类 中的方法org.apache.spark.internal.io.HadoopMapReduceCommitProtocol
-
Abort the job; log and ignore any IO exception thrown.
- abortStagedChanges() - 接口 中的方法org.apache.spark.sql.connector.catalog.StagedTable
-
Abort the changes that were staged, both in metadata and from temporary outputs of this
table's writers.
- abortTask(TaskAttemptContext) - 类 中的方法org.apache.spark.internal.io.FileCommitProtocol
-
Aborts a task after the writes have failed.
- abortTask(TaskAttemptContext) - 类 中的方法org.apache.spark.internal.io.HadoopMapReduceCommitProtocol
-
Abort the task; log and ignore any failure thrown.
- abs(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the absolute value of a numeric value.
- abs(T) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- abs() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- abs(T) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- abs(double) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- abs(float) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- abs(T) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- abs(T) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- abs(T) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- absent() - 类 中的静态方法org.apache.spark.api.java.Optional
-
- AbsoluteError - org.apache.spark.mllib.tree.loss中的类
-
:: DeveloperApi ::
Class for absolute error loss calculation (for regression).
- AbsoluteError() - 类 的构造器org.apache.spark.mllib.tree.loss.AbsoluteError
-
- AbstractLauncher<T extends AbstractLauncher<T>> - org.apache.spark.launcher中的类
-
Base class for launcher implementations.
- accept(Parsers) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- accept(ES, Function1<ES, List<Object>>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- accept(String, PartialFunction<Object, U>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- accept(Path) - 类 中的方法org.apache.spark.ml.image.SamplePathFilter
-
- acceptIf(Function1<Object, Object>, Function1<Object, String>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- acceptMatch(String, PartialFunction<Object, U>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- acceptSeq(ES, Function1<ES, Iterable<Object>>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- acceptsType(DataType) - 类 中的方法org.apache.spark.sql.types.ObjectType
-
- accId() - 类 中的方法org.apache.spark.CleanAccum
-
- accumCleaned(long) - 接口 中的方法org.apache.spark.CleanerListener
-
- AccumulableInfo - org.apache.spark.scheduler中的类
-
:: DeveloperApi ::
Information about an
AccumulatorV2 modified during a task or stage.
- AccumulableInfo - org.apache.spark.status.api.v1中的类
-
- accumulableInfoFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- accumulableInfoToJson(AccumulableInfo) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- accumulables() - 类 中的方法org.apache.spark.scheduler.StageInfo
-
Terminal values of accumulables updated during this stage, including all the user-defined
accumulators.
- accumulables() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
Intermediate updates to accumulables during this task.
- accumulablesToJson(Iterable<AccumulableInfo>) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- AccumulatorContext - org.apache.spark.util中的类
-
An internal class used to track accumulators by Spark itself.
- AccumulatorContext() - 类 的构造器org.apache.spark.util.AccumulatorContext
-
- ACCUMULATORS() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- accumulatorUpdates() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- accumulatorUpdates() - 类 中的方法org.apache.spark.status.api.v1.TaskData
-
- AccumulatorV2<IN,OUT> - org.apache.spark.util中的类
-
The base class for accumulators, that can accumulate inputs of type IN, and produce output of
type OUT.
- AccumulatorV2() - 类 的构造器org.apache.spark.util.AccumulatorV2
-
- accumUpdates() - 类 中的方法org.apache.spark.ExceptionFailure
-
- accumUpdates() - 类 中的方法org.apache.spark.scheduler.SparkListenerExecutorMetricsUpdate
-
- accumUpdates() - 类 中的方法org.apache.spark.TaskKilled
-
- accuracy() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionSummary
-
Returns accuracy.
- accuracy() - 类 中的方法org.apache.spark.mllib.evaluation.MulticlassMetrics
-
- accuracy() - 类 中的方法org.apache.spark.mllib.evaluation.MultilabelMetrics
-
Returns accuracy
- ACLS_ENABLE() - 类 中的静态方法org.apache.spark.internal.config.UI
-
- acos(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
- acos(String) - 类 中的静态方法org.apache.spark.sql.functions
-
- acquire(Seq<String>) - 接口 中的方法org.apache.spark.resource.ResourceAllocator
-
Acquire a sequence of resource addresses (to a launched task), these addresses must be
available.
- ActivationFunction - org.apache.spark.ml.ann中的接口
-
Trait for functions and their derivatives for functional layers
- active() - 类 中的静态方法org.apache.spark.sql.SparkSession
-
Returns the currently active SparkSession, otherwise the default one.
- active() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryManager
-
Returns a list of active queries associated with this SQLContext
- active() - 类 中的方法org.apache.spark.streaming.scheduler.ReceiverInfo
-
- ACTIVE() - 类 中的静态方法org.apache.spark.streaming.scheduler.ReceiverState
-
- activeStages() - 类 中的方法org.apache.spark.status.LiveJob
-
- activeTasks() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- activeTasks() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- activeTasks() - 类 中的方法org.apache.spark.status.LiveJob
-
- activeTasks() - 类 中的方法org.apache.spark.status.LiveStage
-
- activeTasksPerExecutor() - 类 中的方法org.apache.spark.status.LiveStage
-
- add(Vector) - 类 中的方法org.apache.spark.ml.clustering.ExpectationAggregator
-
Add a new training instance to this ExpectationAggregator, update the weights,
means and covariances for each distributions, and update the log likelihood.
- add(Term) - 类 中的静态方法org.apache.spark.ml.feature.Dot
-
- add(Term) - 类 中的静态方法org.apache.spark.ml.feature.EmptyTerm
-
- add(Term) - 接口 中的方法org.apache.spark.ml.feature.Term
-
Creates a summation term by concatenation of terms.
- add(Datum) - 接口 中的方法org.apache.spark.ml.optim.aggregator.DifferentiableLossAggregator
-
Add a single data point to this aggregator.
- add(AFTPoint) - 类 中的方法org.apache.spark.ml.regression.AFTAggregator
-
Add a new training data to this AFTAggregator, and update the loss and gradient
of the objective function.
- add(double[], MultivariateGaussian[], ExpectationSum, Vector<Object>) - 类 中的静态方法org.apache.spark.mllib.clustering.ExpectationSum
-
- add(Vector) - 类 中的方法org.apache.spark.mllib.feature.IDF.DocumentFrequencyAggregator
-
Adds a new document.
- add(BlockMatrix) - 类 中的方法org.apache.spark.mllib.linalg.distributed.BlockMatrix
-
Adds the given block matrix other to this block matrix: this + other.
- add(Vector) - 类 中的方法org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
-
Add a new sample to this summarizer, and update the statistical summary.
- add(StructField) - 类 中的方法org.apache.spark.sql.types.StructType
-
- add(String, DataType) - 类 中的方法org.apache.spark.sql.types.StructType
-
Creates a new
StructType by adding a new nullable field with no metadata.
- add(String, DataType, boolean) - 类 中的方法org.apache.spark.sql.types.StructType
-
Creates a new
StructType by adding a new field with no metadata.
- add(String, DataType, boolean, Metadata) - 类 中的方法org.apache.spark.sql.types.StructType
-
Creates a new
StructType by adding a new field and specifying metadata.
- add(String, DataType, boolean, String) - 类 中的方法org.apache.spark.sql.types.StructType
-
Creates a new
StructType by adding a new field and specifying metadata.
- add(String, String) - 类 中的方法org.apache.spark.sql.types.StructType
-
Creates a new
StructType by adding a new nullable field with no metadata where the
dataType is specified as a String.
- add(String, String, boolean) - 类 中的方法org.apache.spark.sql.types.StructType
-
Creates a new
StructType by adding a new field with no metadata where the
dataType is specified as a String.
- add(String, String, boolean, Metadata) - 类 中的方法org.apache.spark.sql.types.StructType
-
Creates a new
StructType by adding a new field and specifying metadata where the
dataType is specified as a String.
- add(String, String, boolean, String) - 类 中的方法org.apache.spark.sql.types.StructType
-
Creates a new
StructType by adding a new field and specifying metadata where the
dataType is specified as a String.
- add(long, long) - 类 中的静态方法org.apache.spark.streaming.util.RawTextHelper
-
- add(IN) - 类 中的方法org.apache.spark.util.AccumulatorV2
-
Takes the inputs and accumulates.
- add(T) - 类 中的方法org.apache.spark.util.CollectionAccumulator
-
- add(Double) - 类 中的方法org.apache.spark.util.DoubleAccumulator
-
Adds v to the accumulator, i.e. increment sum by v and count by 1.
- add(double) - 类 中的方法org.apache.spark.util.DoubleAccumulator
-
Adds v to the accumulator, i.e. increment sum by v and count by 1.
- add(Long) - 类 中的方法org.apache.spark.util.LongAccumulator
-
Adds v to the accumulator, i.e. increment sum by v and count by 1.
- add(long) - 类 中的方法org.apache.spark.util.LongAccumulator
-
Adds v to the accumulator, i.e. increment sum by v and count by 1.
- add(Object) - 类 中的方法org.apache.spark.util.sketch.CountMinSketch
-
Increments item's count by one.
- add(Object, long) - 类 中的方法org.apache.spark.util.sketch.CountMinSketch
-
Increments item's count by count.
- add_months(Column, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the date that is numMonths after startDate.
- add_months(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the date that is numMonths after startDate.
- addAppArgs(String...) - 类 中的方法org.apache.spark.launcher.AbstractLauncher
-
Adds command line arguments for the application.
- addAppArgs(String...) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
- addBinary(byte[]) - 类 中的方法org.apache.spark.util.sketch.CountMinSketch
-
Increments item's count by one.
- addBinary(byte[], long) - 类 中的方法org.apache.spark.util.sketch.CountMinSketch
-
Increments item's count by count.
- addColumn(String[], DataType) - 接口 中的静态方法org.apache.spark.sql.connector.catalog.TableChange
-
Create a TableChange for adding an optional column.
- addColumn(String[], DataType, boolean) - 接口 中的静态方法org.apache.spark.sql.connector.catalog.TableChange
-
Create a TableChange for adding a column.
- addColumn(String[], DataType, boolean, String) - 接口 中的静态方法org.apache.spark.sql.connector.catalog.TableChange
-
Create a TableChange for adding a column.
- addDirectory(String, File) - 接口 中的方法org.apache.spark.rpc.RpcEnvFileServer
-
Adds a local directory to be served via this file server.
- addFile(String) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Add a file to be downloaded with this Spark job on every node.
- addFile(String, boolean) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Add a file to be downloaded with this Spark job on every node.
- addFile(String) - 类 中的方法org.apache.spark.launcher.AbstractLauncher
-
Adds a file to be submitted with the application.
- addFile(String) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
- addFile(File) - 接口 中的方法org.apache.spark.rpc.RpcEnvFileServer
-
Adds a file to be served by this RpcEnv.
- addFile(String) - 类 中的方法org.apache.spark.SparkContext
-
Add a file to be downloaded with this Spark job on every node.
- addFile(String, boolean) - 类 中的方法org.apache.spark.SparkContext
-
Add a file to be downloaded with this Spark job on every node.
- addFilter(ServletContextHandler, String, Map<String, String>) - 类 中的静态方法org.apache.spark.ui.JettyUtils
-
- addGrid(Param<T>, Iterable<T>) - 类 中的方法org.apache.spark.ml.tuning.ParamGridBuilder
-
Adds a param with multiple values (overwrites if the input param exists).
- addGrid(DoubleParam, double[]) - 类 中的方法org.apache.spark.ml.tuning.ParamGridBuilder
-
Adds a double param with multiple values.
- addGrid(IntParam, int[]) - 类 中的方法org.apache.spark.ml.tuning.ParamGridBuilder
-
Adds an int param with multiple values.
- addGrid(FloatParam, float[]) - 类 中的方法org.apache.spark.ml.tuning.ParamGridBuilder
-
Adds a float param with multiple values.
- addGrid(LongParam, long[]) - 类 中的方法org.apache.spark.ml.tuning.ParamGridBuilder
-
Adds a long param with multiple values.
- addGrid(BooleanParam) - 类 中的方法org.apache.spark.ml.tuning.ParamGridBuilder
-
Adds a boolean param with true and false.
- addJar(String) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Adds a JAR dependency for all tasks to be executed on this SparkContext in the future.
- addJar(String) - 类 中的方法org.apache.spark.launcher.AbstractLauncher
-
Adds a jar file to be submitted with the application.
- addJar(String) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
- addJar(File) - 接口 中的方法org.apache.spark.rpc.RpcEnvFileServer
-
Adds a jar to be served by this RpcEnv.
- addJar(String) - 类 中的方法org.apache.spark.SparkContext
-
Adds a JAR dependency for all tasks to be executed on this SparkContext in the future.
- addJar(String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Add a jar into class loader
- addJar(String) - 类 中的方法org.apache.spark.sql.hive.HiveSessionResourceLoader
-
- addListener(SparkAppHandle.Listener) - 接口 中的方法org.apache.spark.launcher.SparkAppHandle
-
Adds a listener to be notified of changes to the handle's information.
- addListener(StreamingQueryListener) - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryManager
-
- addListener(L) - 接口 中的方法org.apache.spark.util.ListenerBus
-
Add a listener to listen events.
- addLocalConfiguration(String, int, int, int, JobConf) - 类 中的静态方法org.apache.spark.rdd.HadoopRDD
-
Add Hadoop configuration specific to a single partition and attempt.
- addLong(long) - 类 中的方法org.apache.spark.util.sketch.CountMinSketch
-
Increments item's count by one.
- addLong(long, long) - 类 中的方法org.apache.spark.util.sketch.CountMinSketch
-
Increments item's count by count.
- addMapOutput(int, MapStatus) - 类 中的方法org.apache.spark.ShuffleStatus
-
Register a map output.
- addMetrics(TaskMetrics, TaskMetrics) - 类 中的静态方法org.apache.spark.status.LiveEntityHelpers
-
Add m2 values to m1.
- addPartition(LiveRDDPartition) - 类 中的方法org.apache.spark.status.RDDPartitionSeq
-
- addPartToPGroup(Partition, PartitionGroup) - 类 中的方法org.apache.spark.rdd.DefaultPartitionCoalescer
-
- addPyFile(String) - 类 中的方法org.apache.spark.launcher.AbstractLauncher
-
Adds a python file / zip / egg to be submitted with the application.
- addPyFile(String) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
- address() - 类 中的方法org.apache.spark.BarrierTaskInfo
-
- address() - 类 中的方法org.apache.spark.status.api.v1.RDDDataDistribution
-
- addresses() - 类 中的方法org.apache.spark.resource.ResourceInformation
-
- addresses() - 类 中的方法org.apache.spark.resource.ResourceInformationJson
-
- addSchedulable(Schedulable) - 接口 中的方法org.apache.spark.scheduler.Schedulable
-
- addShutdownHook(Function0<BoxedUnit>) - 类 中的静态方法org.apache.spark.util.ShutdownHookManager
-
Adds a shutdown hook with default priority.
- addShutdownHook(int, Function0<BoxedUnit>) - 类 中的静态方法org.apache.spark.util.ShutdownHookManager
-
Adds a shutdown hook with the given priority.
- addSparkArg(String) - 类 中的方法org.apache.spark.launcher.AbstractLauncher
-
Adds a no-value argument to the Spark invocation.
- addSparkArg(String, String) - 类 中的方法org.apache.spark.launcher.AbstractLauncher
-
Adds an argument with a value to the Spark invocation.
- addSparkArg(String) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
- addSparkArg(String, String) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
- addSparkListener(SparkListenerInterface) - 类 中的方法org.apache.spark.SparkContext
-
:: DeveloperApi ::
Register a listener to receive up-calls from events that happen during execution.
- addSparkVersionMetadata(RecordWriter<NullWritable, Writable>) - 类 中的静态方法org.apache.spark.sql.hive.orc.OrcFileFormat
-
Add a metadata specifying Spark version.
- addStreamingListener(StreamingListener) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
- addStreamingListener(StreamingListener) - 类 中的方法org.apache.spark.streaming.StreamingContext
-
- addString(String) - 类 中的方法org.apache.spark.util.sketch.CountMinSketch
-
Increments item's count by one.
- addString(String, long) - 类 中的方法org.apache.spark.util.sketch.CountMinSketch
-
Increments item's count by count.
- addTaskCompletionListener(TaskCompletionListener) - 类 中的方法org.apache.spark.BarrierTaskContext
-
- addTaskCompletionListener(TaskCompletionListener) - 类 中的方法org.apache.spark.TaskContext
-
Adds a (Java friendly) listener to be executed on task completion.
- addTaskCompletionListener(Function1<TaskContext, U>) - 类 中的方法org.apache.spark.TaskContext
-
Adds a listener in the form of a Scala closure to be executed on task completion.
- addTaskFailureListener(TaskFailureListener) - 类 中的方法org.apache.spark.BarrierTaskContext
-
- addTaskFailureListener(TaskFailureListener) - 类 中的方法org.apache.spark.TaskContext
-
Adds a listener to be executed on task failure.
- addTaskFailureListener(Function2<TaskContext, Throwable, BoxedUnit>) - 类 中的方法org.apache.spark.TaskContext
-
Adds a listener to be executed on task failure.
- addTaskSetManager(Schedulable, Properties) - 接口 中的方法org.apache.spark.scheduler.SchedulableBuilder
-
- addTime() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- addTime() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- addURL(URL) - 类 中的方法org.apache.spark.util.MutableURLClassLoader
-
- AddWebUIFilter(String, Map<String, String>, String) - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.AddWebUIFilter
-
- AddWebUIFilter$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.AddWebUIFilter$
-
- ADMIN_ACLS() - 类 中的静态方法org.apache.spark.internal.config.UI
-
- ADMIN_ACLS_GROUPS() - 类 中的静态方法org.apache.spark.internal.config.UI
-
- AFTAggregator - org.apache.spark.ml.regression中的类
-
AFTAggregator computes the gradient and loss for a AFT loss function,
as used in AFT survival regression for samples in sparse or dense vector in an online fashion.
- AFTAggregator(Broadcast<DenseVector<Object>>, boolean, Broadcast<double[]>) - 类 的构造器org.apache.spark.ml.regression.AFTAggregator
-
- AFTCostFun - org.apache.spark.ml.regression中的类
-
AFTCostFun implements Breeze's DiffFunction[T] for AFT cost.
- AFTCostFun(RDD<AFTPoint>, boolean, Broadcast<double[]>, int) - 类 的构造器org.apache.spark.ml.regression.AFTCostFun
-
- AFTSurvivalRegression - org.apache.spark.ml.regression中的类
-
- AFTSurvivalRegression(String) - 类 的构造器org.apache.spark.ml.regression.AFTSurvivalRegression
-
- AFTSurvivalRegression() - 类 的构造器org.apache.spark.ml.regression.AFTSurvivalRegression
-
- AFTSurvivalRegressionModel - org.apache.spark.ml.regression中的类
-
- AFTSurvivalRegressionParams - org.apache.spark.ml.regression中的接口
-
Params for accelerated failure time (AFT) regression.
- agg(Column, Column...) - 类 中的方法org.apache.spark.sql.Dataset
-
Aggregates on the entire Dataset without groups.
- agg(Tuple2<String, String>, Seq<Tuple2<String, String>>) - 类 中的方法org.apache.spark.sql.Dataset
-
(Scala-specific) Aggregates on the entire Dataset without groups.
- agg(Map<String, String>) - 类 中的方法org.apache.spark.sql.Dataset
-
(Scala-specific) Aggregates on the entire Dataset without groups.
- agg(Map<String, String>) - 类 中的方法org.apache.spark.sql.Dataset
-
(Java-specific) Aggregates on the entire Dataset without groups.
- agg(Column, Seq<Column>) - 类 中的方法org.apache.spark.sql.Dataset
-
Aggregates on the entire Dataset without groups.
- agg(TypedColumn<V, U1>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
Computes the given aggregation, returning a
Dataset of tuples for each unique key
and the result of computing this aggregation over all elements in the group.
- agg(TypedColumn<V, U1>, TypedColumn<V, U2>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
Computes the given aggregations, returning a
Dataset of tuples for each unique key
and the result of computing these aggregations over all elements in the group.
- agg(TypedColumn<V, U1>, TypedColumn<V, U2>, TypedColumn<V, U3>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
Computes the given aggregations, returning a
Dataset of tuples for each unique key
and the result of computing these aggregations over all elements in the group.
- agg(TypedColumn<V, U1>, TypedColumn<V, U2>, TypedColumn<V, U3>, TypedColumn<V, U4>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
Computes the given aggregations, returning a
Dataset of tuples for each unique key
and the result of computing these aggregations over all elements in the group.
- agg(TypedColumn<V, U1>, TypedColumn<V, U2>, TypedColumn<V, U3>, TypedColumn<V, U4>, TypedColumn<V, U5>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
Computes the given aggregations, returning a
Dataset of tuples for each unique key
and the result of computing these aggregations over all elements in the group.
- agg(TypedColumn<V, U1>, TypedColumn<V, U2>, TypedColumn<V, U3>, TypedColumn<V, U4>, TypedColumn<V, U5>, TypedColumn<V, U6>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
Computes the given aggregations, returning a
Dataset of tuples for each unique key
and the result of computing these aggregations over all elements in the group.
- agg(TypedColumn<V, U1>, TypedColumn<V, U2>, TypedColumn<V, U3>, TypedColumn<V, U4>, TypedColumn<V, U5>, TypedColumn<V, U6>, TypedColumn<V, U7>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
Computes the given aggregations, returning a
Dataset of tuples for each unique key
and the result of computing these aggregations over all elements in the group.
- agg(TypedColumn<V, U1>, TypedColumn<V, U2>, TypedColumn<V, U3>, TypedColumn<V, U4>, TypedColumn<V, U5>, TypedColumn<V, U6>, TypedColumn<V, U7>, TypedColumn<V, U8>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
Computes the given aggregations, returning a
Dataset of tuples for each unique key
and the result of computing these aggregations over all elements in the group.
- agg(Column, Column...) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Compute aggregates by specifying a series of aggregate columns.
- agg(Tuple2<String, String>, Seq<Tuple2<String, String>>) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
(Scala-specific) Compute aggregates by specifying the column names and
aggregate methods.
- agg(Map<String, String>) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
(Scala-specific) Compute aggregates by specifying a map from column name to
aggregate methods.
- agg(Map<String, String>) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
(Java-specific) Compute aggregates by specifying a map from column name to
aggregate methods.
- agg(Column, Seq<Column>) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Compute aggregates by specifying a series of aggregate columns.
- aggregate(U, Function2<U, T, U>, Function2<U, U, U>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Aggregate the elements of each partition, and then the results for all the partitions, using
given combine functions and a neutral "zero value".
- aggregate(U, Function2<U, T, U>, Function2<U, U, U>, ClassTag<U>) - 类 中的方法org.apache.spark.rdd.RDD
-
Aggregate the elements of each partition, and then the results for all the partitions, using
given combine functions and a neutral "zero value".
- aggregate(Column, Column, Function2<Column, Column, Column>, Function1<Column, Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Applies a binary operator to an initial state and all elements in the array,
and reduces this to a single state.
- aggregate(Column, Column, Function2<Column, Column, Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Applies a binary operator to an initial state and all elements in the array,
and reduces this to a single state.
- aggregateByKey(U, Partitioner, Function2<U, V, U>, Function2<U, U, U>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Aggregate the values of each key, using given combine functions and a neutral "zero value".
- aggregateByKey(U, int, Function2<U, V, U>, Function2<U, U, U>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Aggregate the values of each key, using given combine functions and a neutral "zero value".
- aggregateByKey(U, Function2<U, V, U>, Function2<U, U, U>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Aggregate the values of each key, using given combine functions and a neutral "zero value".
- aggregateByKey(U, Partitioner, Function2<U, V, U>, Function2<U, U, U>, ClassTag<U>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Aggregate the values of each key, using given combine functions and a neutral "zero value".
- aggregateByKey(U, int, Function2<U, V, U>, Function2<U, U, U>, ClassTag<U>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Aggregate the values of each key, using given combine functions and a neutral "zero value".
- aggregateByKey(U, Function2<U, V, U>, Function2<U, U, U>, ClassTag<U>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Aggregate the values of each key, using given combine functions and a neutral "zero value".
- AggregatedDialect - org.apache.spark.sql.jdbc中的类
-
AggregatedDialect can unify multiple dialects into one virtual Dialect.
- AggregatedDialect(List<JdbcDialect>) - 类 的构造器org.apache.spark.sql.jdbc.AggregatedDialect
-
- aggregateMessages(Function1<EdgeContext<VD, ED, A>, BoxedUnit>, Function2<A, A, A>, TripletFields, ClassTag<A>) - 类 中的方法org.apache.spark.graphx.Graph
-
Aggregates values from the neighboring edges and vertices of each vertex.
- aggregateMessagesWithActiveSet(Function1<EdgeContext<VD, ED, A>, BoxedUnit>, Function2<A, A, A>, TripletFields, Option<Tuple2<VertexRDD<?>, EdgeDirection>>, ClassTag<A>) - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- aggregateUsingIndex(RDD<Tuple2<Object, VD2>>, Function2<VD2, VD2, VD2>, ClassTag<VD2>) - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- aggregateUsingIndex(RDD<Tuple2<Object, VD2>>, Function2<VD2, VD2, VD2>, ClassTag<VD2>) - 类 中的方法org.apache.spark.graphx.VertexRDD
-
Aggregates vertices in messages that have the same ids using reduceFunc, returning a
VertexRDD co-indexed with this.
- AggregatingEdgeContext<VD,ED,A> - org.apache.spark.graphx.impl中的类
-
- AggregatingEdgeContext(Function2<A, A, A>, Object, BitSet) - 类 的构造器org.apache.spark.graphx.impl.AggregatingEdgeContext
-
- aggregationDepth() - 类 中的方法org.apache.spark.ml.classification.LinearSVC
-
- aggregationDepth() - 类 中的方法org.apache.spark.ml.classification.LinearSVCModel
-
- aggregationDepth() - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
- aggregationDepth() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- aggregationDepth() - 接口 中的方法org.apache.spark.ml.param.shared.HasAggregationDepth
-
Param for suggested depth for treeAggregate (>= 2).
- aggregationDepth() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- aggregationDepth() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- aggregationDepth() - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
- aggregationDepth() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionModel
-
- Aggregator<K,V,C> - org.apache.spark中的类
-
:: DeveloperApi ::
A set of functions used to aggregate data.
- Aggregator(Function1<V, C>, Function2<C, V, C>, Function2<C, C, C>) - 类 的构造器org.apache.spark.Aggregator
-
- aggregator() - 类 中的方法org.apache.spark.ShuffleDependency
-
- Aggregator<IN,BUF,OUT> - org.apache.spark.sql.expressions中的类
-
A base class for user-defined aggregations, which can be used in Dataset operations to take
all of the elements of a group and reduce them to a single value.
- Aggregator() - 类 的构造器org.apache.spark.sql.expressions.Aggregator
-
- aic(RDD<Tuple3<Object, Object, Object>>, double, double, double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Binomial$
-
- aic(RDD<Tuple3<Object, Object, Object>>, double, double, double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Gamma$
-
- aic(RDD<Tuple3<Object, Object, Object>>, double, double, double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Gaussian$
-
- aic(RDD<Tuple3<Object, Object, Object>>, double, double, double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Poisson$
-
- aic() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary
-
- Algo - org.apache.spark.mllib.tree.configuration中的类
-
Enum to select the algorithm for the decision tree
- Algo() - 类 的构造器org.apache.spark.mllib.tree.configuration.Algo
-
- algo() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- algo() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel
-
- algo() - 类 中的方法org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
-
- algo() - 类 中的方法org.apache.spark.mllib.tree.model.RandomForestModel
-
- algorithm() - 类 中的方法org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
-
- alias(String) - 类 中的方法org.apache.spark.sql.Column
-
Gives the column an alias.
- alias(String) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset with an alias set.
- alias(Symbol) - 类 中的方法org.apache.spark.sql.Dataset
-
(Scala-specific) Returns a new Dataset with an alias set.
- All - 类 中的静态变量org.apache.spark.graphx.TripletFields
-
Expose all the fields (source, edge, and destination).
- AllJobsCancelled - org.apache.spark.scheduler中的类
-
- AllJobsCancelled() - 类 的构造器org.apache.spark.scheduler.AllJobsCancelled
-
- allocator() - 类 中的方法org.apache.spark.storage.memory.SerializedValuesHolder
-
- AllReceiverIds - org.apache.spark.streaming.scheduler中的类
-
A message used by ReceiverTracker to ask all receiver's ids still stored in
ReceiverTrackerEndpoint.
- AllReceiverIds() - 类 的构造器org.apache.spark.streaming.scheduler.AllReceiverIds
-
- allSources() - 类 中的静态方法org.apache.spark.metrics.source.StaticSources
-
The set of all static sources.
- alpha() - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- alpha() - 接口 中的方法org.apache.spark.ml.recommendation.ALSParams
-
Param for the alpha parameter in the implicit preference formulation (nonnegative).
- alpha() - 类 中的方法org.apache.spark.mllib.random.WeibullGenerator
-
- ALS - org.apache.spark.ml.recommendation中的类
-
Alternating Least Squares (ALS) matrix factorization.
- ALS(String) - 类 的构造器org.apache.spark.ml.recommendation.ALS
-
- ALS() - 类 的构造器org.apache.spark.ml.recommendation.ALS
-
- ALS - org.apache.spark.mllib.recommendation中的类
-
Alternating Least Squares matrix factorization.
- ALS() - 类 的构造器org.apache.spark.mllib.recommendation.ALS
-
Constructs an ALS instance with default parameters: {numBlocks: -1, rank: 10, iterations: 10,
lambda: 0.01, implicitPrefs: false, alpha: 1.0}.
- ALS.InBlock$ - org.apache.spark.ml.recommendation中的类
-
- ALS.LeastSquaresNESolver - org.apache.spark.ml.recommendation中的接口
-
Trait for least squares solvers applied to the normal equation.
- ALS.Rating<ID> - org.apache.spark.ml.recommendation中的类
-
:: DeveloperApi ::
Rating class for better code readability.
- ALS.Rating$ - org.apache.spark.ml.recommendation中的类
-
- ALS.RatingBlock$ - org.apache.spark.ml.recommendation中的类
-
- ALSModel - org.apache.spark.ml.recommendation中的类
-
Model fitted by ALS.
- ALSModelParams - org.apache.spark.ml.recommendation中的接口
-
Common params for ALS and ALSModel.
- ALSParams - org.apache.spark.ml.recommendation中的接口
-
Common params for ALS.
- alterDatabase(CatalogDatabase) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Alter a database whose name matches the one specified in database, assuming it exists.
- alterFunction(String, CatalogFunction) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Alter a function whose name matches the one specified in `func`, assuming it exists.
- alterNamespace(String[], NamespaceChange...) - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- alterNamespace(String[], NamespaceChange...) - 接口 中的方法org.apache.spark.sql.connector.catalog.SupportsNamespaces
-
Apply a set of metadata changes to a namespace in the catalog.
- alterPartitions(String, String, Seq<CatalogTablePartition>) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Alter one or more table partitions whose specs match the ones specified in newParts,
assuming the partitions exist.
- alterTable(Identifier, TableChange...) - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- alterTable(Identifier, TableChange...) - 接口 中的方法org.apache.spark.sql.connector.catalog.TableCatalog
-
Apply a set of
changes to a table in the catalog.
- alterTable(CatalogTable) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Alter a table whose name matches the one specified in `table`, assuming it exists.
- alterTable(String, String, CatalogTable) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Updates the given table with new metadata, optionally renaming the table or
moving across different database.
- alterTableDataSchema(String, String, StructType, Map<String, String>) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Updates the given table with a new data schema and table properties, and keep everything else
unchanged.
- AlwaysFalse - org.apache.spark.sql.sources中的类
-
A filter that always evaluates to false.
- AlwaysFalse() - 类 的构造器org.apache.spark.sql.sources.AlwaysFalse
-
- AlwaysTrue - org.apache.spark.sql.sources中的类
-
A filter that always evaluates to true.
- AlwaysTrue() - 类 的构造器org.apache.spark.sql.sources.AlwaysTrue
-
- am() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterClusterManager
-
- AMOUNT() - 类 中的静态方法org.apache.spark.resource.ResourceUtils
-
- AnalysisException - org.apache.spark.sql中的异常错误
-
Thrown when a query fails to analyze, usually because the query itself is invalid.
- and(Column) - 类 中的方法org.apache.spark.sql.Column
-
Boolean AND.
- And - org.apache.spark.sql.sources中的类
-
A filter that evaluates to true iff both left or right evaluate to true.
- And(Filter, Filter) - 类 的构造器org.apache.spark.sql.sources.And
-
- antecedent() - 类 中的方法org.apache.spark.mllib.fpm.AssociationRules.Rule
-
- ANY() - 类 中的静态方法org.apache.spark.scheduler.TaskLocality
-
- AnyDataType - org.apache.spark.sql.types中的类
-
An AbstractDataType that matches any concrete data types.
- AnyDataType() - 类 的构造器org.apache.spark.sql.types.AnyDataType
-
- anyNull() - 接口 中的方法org.apache.spark.sql.Row
-
Returns true if there are any NULL values in this row.
- anyNull() - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- ApiHelper - org.apache.spark.ui.jobs中的类
-
- ApiHelper() - 类 的构造器org.apache.spark.ui.jobs.ApiHelper
-
- ApiRequestContext - org.apache.spark.status.api.v1中的接口
-
- APP_DATA_RETENTION() - 类 中的静态方法org.apache.spark.internal.config.Worker
-
- APP_STATUS_METRICS_ENABLED() - 类 中的静态方法org.apache.spark.internal.config.Status
-
- appAttemptId() - 类 中的方法org.apache.spark.scheduler.SparkListenerApplicationStart
-
- append() - 类 中的方法org.apache.spark.sql.DataFrameWriterV2
-
Append the contents of the data frame to the output table.
- Append() - 类 中的静态方法org.apache.spark.sql.streaming.OutputMode
-
OutputMode in which only the new rows in the streaming DataFrame/Dataset will be
written to the sink.
- appendBias(Vector) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Returns a new vector with 1.0 (bias) appended to the input vector.
- appendColumn(StructType, String, DataType, boolean) - 类 中的静态方法org.apache.spark.ml.util.SchemaUtils
-
Appends a new column to the input schema.
- appendColumn(StructType, StructField) - 类 中的静态方法org.apache.spark.ml.util.SchemaUtils
-
Appends a new column to the input schema.
- appendReadColumns(Configuration, Seq<Integer>, Seq<String>) - 类 中的静态方法org.apache.spark.sql.hive.HiveShim
-
- AppHistoryServerPlugin - org.apache.spark.status中的接口
-
An interface for creating history listeners(to replay event logs) defined in other modules like
SQL, and setup the UI of the plugin to rebuild the history UI.
- appId() - 类 中的方法org.apache.spark.scheduler.SparkListenerApplicationStart
-
- appId() - 接口 中的方法org.apache.spark.status.api.v1.BaseAppResource
-
- APPLICATION_EXECUTOR_LIMIT() - 类 中的静态方法org.apache.spark.ui.ToolTips
-
- APPLICATION_MASTER() - 类 中的静态方法org.apache.spark.metrics.MetricsSystemInstances
-
- applicationAttemptId() - 接口 中的方法org.apache.spark.scheduler.SchedulerBackend
-
Get the attempt ID for this run, if the cluster manager supports multiple
attempts.
- applicationAttemptId() - 接口 中的方法org.apache.spark.scheduler.TaskScheduler
-
Get an application's attempt ID associated with the job.
- applicationAttemptId() - 类 中的方法org.apache.spark.SparkContext
-
- ApplicationAttemptInfo - org.apache.spark.status.api.v1中的类
-
- applicationEndFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- applicationEndToJson(SparkListenerApplicationEnd) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- ApplicationEnvironmentInfo - org.apache.spark.status.api.v1中的类
-
- applicationId() - 接口 中的方法org.apache.spark.scheduler.SchedulerBackend
-
Get an application ID associated with the job.
- applicationId() - 接口 中的方法org.apache.spark.scheduler.TaskScheduler
-
Get an application ID associated with the job.
- applicationId() - 类 中的方法org.apache.spark.SparkContext
-
A unique identifier for the Spark application.
- ApplicationInfo - org.apache.spark.status.api.v1中的类
-
- APPLICATIONS() - 类 中的静态方法org.apache.spark.metrics.MetricsSystemInstances
-
- applicationStartFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- applicationStartToJson(SparkListenerApplicationStart) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- ApplicationStatus - org.apache.spark.status.api.v1中的枚举
-
- apply(T1) - 类 中的静态方法org.apache.spark.CleanAccum
-
- apply(T1) - 类 中的静态方法org.apache.spark.CleanBroadcast
-
- apply(T1) - 类 中的静态方法org.apache.spark.CleanCheckpoint
-
- apply(T1) - 类 中的静态方法org.apache.spark.CleanRDD
-
- apply(T1) - 类 中的静态方法org.apache.spark.CleanShuffle
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.ContextBarrierId
-
- apply(T1, T2, T3, T4, T5, T6, T7, T8) - 类 中的静态方法org.apache.spark.ExceptionFailure
-
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.ExecutorLostFailure
-
- apply(T1) - 类 中的静态方法org.apache.spark.ExecutorRegistered
-
- apply(T1) - 类 中的静态方法org.apache.spark.ExecutorRemoved
-
- apply(T1, T2, T3, T4, T5, T6) - 类 中的静态方法org.apache.spark.FetchFailed
-
- apply(RDD<Tuple2<Object, VD>>, RDD<Edge<ED>>, VD, StorageLevel, StorageLevel, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.Graph
-
Construct a graph from a collection of vertices and
edges with attributes.
- apply(RDD<Edge<ED>>, VD, StorageLevel, StorageLevel, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.impl.GraphImpl
-
Create a graph from edges, setting referenced vertices to defaultVertexAttr.
- apply(RDD<Tuple2<Object, VD>>, RDD<Edge<ED>>, VD, StorageLevel, StorageLevel, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.impl.GraphImpl
-
Create a graph from vertices and edges, setting missing vertices to defaultVertexAttr.
- apply(VertexRDD<VD>, EdgeRDD<ED>, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.impl.GraphImpl
-
Create a graph from a VertexRDD and an EdgeRDD with arbitrary replicated vertices.
- apply(Graph<VD, ED>, A, int, EdgeDirection, Function3<Object, VD, A, VD>, Function1<EdgeTriplet<VD, ED>, Iterator<Tuple2<Object, A>>>, Function2<A, A, A>, ClassTag<VD>, ClassTag<ED>, ClassTag<A>) - 类 中的静态方法org.apache.spark.graphx.Pregel
-
Execute a Pregel-like iterative vertex-parallel abstraction.
- apply(RDD<Tuple2<Object, VD>>, ClassTag<VD>) - 类 中的静态方法org.apache.spark.graphx.VertexRDD
-
Constructs a standalone
VertexRDD (one that is not set up for efficient joins with an
EdgeRDD) from an RDD of vertex-attribute pairs.
- apply(RDD<Tuple2<Object, VD>>, EdgeRDD<?>, VD, ClassTag<VD>) - 类 中的静态方法org.apache.spark.graphx.VertexRDD
-
Constructs a VertexRDD from an RDD of vertex-attribute pairs.
- apply(RDD<Tuple2<Object, VD>>, EdgeRDD<?>, VD, Function2<VD, VD, VD>, ClassTag<VD>) - 类 中的静态方法org.apache.spark.graphx.VertexRDD
-
Constructs a VertexRDD from an RDD of vertex-attribute pairs.
- apply(DenseMatrix<Object>, DenseMatrix<Object>, Function1<Object, Object>) - 类 中的静态方法org.apache.spark.ml.ann.ApplyInPlace
-
- apply(DenseMatrix<Object>, DenseMatrix<Object>, DenseMatrix<Object>, Function2<Object, Object, Object>) - 类 中的静态方法org.apache.spark.ml.ann.ApplyInPlace
-
- apply(String) - 类 中的方法org.apache.spark.ml.attribute.AttributeGroup
-
Gets an attribute by its name.
- apply(int) - 类 中的方法org.apache.spark.ml.attribute.AttributeGroup
-
Gets an attribute by its index.
- apply(T1, T2) - 类 中的静态方法org.apache.spark.ml.clustering.ClusterData
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.ml.feature.LabeledPoint
-
- apply(int, int) - 类 中的方法org.apache.spark.ml.linalg.DenseMatrix
-
- apply(int) - 类 中的方法org.apache.spark.ml.linalg.DenseVector
-
- apply(int, int) - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Gets the (i, j)-th element.
- apply(int, int) - 类 中的方法org.apache.spark.ml.linalg.SparseMatrix
-
- apply(int) - 类 中的方法org.apache.spark.ml.linalg.SparseVector
-
- apply(int) - 接口 中的方法org.apache.spark.ml.linalg.Vector
-
Gets the value of the ith element.
- apply(Param<T>) - 类 中的方法org.apache.spark.ml.param.ParamMap
-
Gets the value of the input param or its default value if it does not exist.
- apply(GeneralizedLinearRegressionBase) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.FamilyAndLink$
-
Constructs the FamilyAndLink object from a parameter map
- apply(T1) - 类 中的静态方法org.apache.spark.ml.SaveInstanceEnd
-
- apply(T1) - 类 中的静态方法org.apache.spark.ml.SaveInstanceStart
-
- apply() - 类 中的静态方法org.apache.spark.ml.TransformEnd
-
- apply() - 类 中的静态方法org.apache.spark.ml.TransformStart
-
- apply(Split) - 类 中的方法org.apache.spark.ml.tree.DecisionTreeModelReadWrite.SplitData$
-
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.mllib.classification.impl.GLMClassificationModel.SaveLoadV1_0$.Data
-
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV1_0$.Data
-
- apply(T1, T2, T3, T4) - 类 中的静态方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV2_0$.Data
-
- apply(BinaryConfusionMatrix) - 接口 中的方法org.apache.spark.mllib.evaluation.binary.BinaryClassificationMetricComputer
-
- apply(BinaryConfusionMatrix) - 类 中的静态方法org.apache.spark.mllib.evaluation.binary.FalsePositiveRate
-
- apply(BinaryConfusionMatrix) - 类 中的静态方法org.apache.spark.mllib.evaluation.binary.Precision
-
- apply(BinaryConfusionMatrix) - 类 中的静态方法org.apache.spark.mllib.evaluation.binary.Recall
-
- apply(T1) - 类 中的静态方法org.apache.spark.mllib.feature.ChiSqSelectorModel.SaveLoadV1_0$.Data
-
- apply(T1, T2, T3, T4, T5) - 类 中的静态方法org.apache.spark.mllib.feature.VocabWord
-
- apply(int, int) - 类 中的方法org.apache.spark.mllib.linalg.DenseMatrix
-
- apply(int) - 类 中的方法org.apache.spark.mllib.linalg.DenseVector
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.mllib.linalg.distributed.IndexedRow
-
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.mllib.linalg.distributed.MatrixEntry
-
- apply(int, int) - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
Gets the (i, j)-th element.
- apply(int, int) - 类 中的方法org.apache.spark.mllib.linalg.SparseMatrix
-
- apply(int) - 类 中的方法org.apache.spark.mllib.linalg.SparseVector
-
- apply(int) - 接口 中的方法org.apache.spark.mllib.linalg.Vector
-
Gets the value of the ith element.
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.mllib.recommendation.Rating
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.mllib.regression.impl.GLMRegressionModel.SaveLoadV1_0$.Data
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.mllib.stat.test.BinarySample
-
- apply(int) - 类 中的静态方法org.apache.spark.mllib.tree.configuration.Algo
-
- apply(int) - 类 中的静态方法org.apache.spark.mllib.tree.configuration.EnsembleCombiningStrategy
-
- apply(int) - 类 中的静态方法org.apache.spark.mllib.tree.configuration.FeatureType
-
- apply(int) - 类 中的静态方法org.apache.spark.mllib.tree.configuration.QuantileStrategy
-
- apply(int, Node) - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.NodeData$
-
- apply(Row) - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.NodeData$
-
- apply(int, Node) - 类 中的静态方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.NodeData
-
- apply(Row) - 类 中的静态方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.NodeData
-
- apply(Predict) - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.PredictData$
-
- apply(Row) - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.PredictData$
-
- apply(Predict) - 类 中的静态方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.PredictData
-
- apply(Row) - 类 中的静态方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.PredictData
-
- apply(Split) - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.SplitData$
-
- apply(Row) - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.SplitData$
-
- apply(Split) - 类 中的静态方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.SplitData
-
- apply(Row) - 类 中的静态方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.SplitData
-
- apply(int, Predict, double, boolean) - 类 中的静态方法org.apache.spark.mllib.tree.model.Node
-
Construct a node with nodeIndex, predict, impurity and isLeaf parameters.
- apply(T1, T2, T3, T4) - 类 中的静态方法org.apache.spark.mllib.tree.model.Split
-
- apply(int) - 类 中的静态方法org.apache.spark.rdd.CheckpointState
-
- apply(int) - 类 中的静态方法org.apache.spark.rdd.DeterministicLevel
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.resource.ResourceInformationJson
-
- apply(T1, T2, T3, T4, T5, T6, T7) - 类 中的静态方法org.apache.spark.scheduler.AccumulableInfo
-
- apply(T1, T2, T3, T4) - 类 中的静态方法org.apache.spark.scheduler.AskPermissionToCommitOutput
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.scheduler.BlacklistedExecutor
-
- apply(String, long, Enumeration.Value, ByteBuffer, Map<String, ResourceInformation>) - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.StatusUpdate$
-
Alternate factory method that takes a ByteBuffer directly for the data field
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.scheduler.local.KillTask
-
- apply() - 类 中的静态方法org.apache.spark.scheduler.local.ReviveOffers
-
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.scheduler.local.StatusUpdate
-
- apply() - 类 中的静态方法org.apache.spark.scheduler.local.StopExecutor
-
- apply(long, TaskMetrics) - 类 中的静态方法org.apache.spark.scheduler.RuntimePercentage
-
- apply(int) - 类 中的静态方法org.apache.spark.scheduler.SchedulingMode
-
- apply(T1) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerApplicationEnd
-
- apply(T1, T2, T3, T4, T5, T6, T7) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerApplicationStart
-
- apply(T1, T2, T3, T4, T5) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerBlockManagerAdded
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerBlockManagerRemoved
-
- apply(T1) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerBlockUpdated
-
- apply(T1) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerEnvironmentUpdate
-
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerExecutorAdded
-
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerExecutorBlacklisted
-
- apply(T1, T2, T3, T4, T5) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerExecutorBlacklistedForStage
-
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerExecutorMetricsUpdate
-
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerExecutorRemoved
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerExecutorUnblacklisted
-
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerJobEnd
-
- apply(T1, T2, T3, T4) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerJobStart
-
- apply(T1) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerLogStart
-
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerNodeBlacklisted
-
- apply(T1, T2, T3, T4, T5) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerNodeBlacklistedForStage
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerNodeUnblacklisted
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerSpeculativeTaskSubmitted
-
- apply(T1) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerStageCompleted
-
- apply(T1, T2, T3, T4) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerStageExecutorMetrics
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerStageSubmitted
-
- apply(T1, T2, T3, T4, T5, T6, T7) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerTaskEnd
-
- apply(T1) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerTaskGettingResult
-
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerTaskStart
-
- apply(T1) - 类 中的静态方法org.apache.spark.scheduler.SparkListenerUnpersistRDD
-
- apply(int) - 类 中的静态方法org.apache.spark.scheduler.TaskLocality
-
- apply(Object) - 类 中的方法org.apache.spark.sql.Column
-
Extracts a value or values from a complex type.
- apply(String, Expression...) - 类 中的静态方法org.apache.spark.sql.connector.expressions.Expressions
-
Create a logical transform for applying a named transform.
- apply(String, Seq<Expression>) - 类 中的静态方法org.apache.spark.sql.connector.expressions.LogicalExpressions
-
- apply(String) - 类 中的方法org.apache.spark.sql.Dataset
-
Selects column based on the column name and returns it as a
Column.
- apply(LogicalPlan) - 类 中的静态方法org.apache.spark.sql.dynamicpruning.CleanupDynamicPruningFilters
-
- apply(LogicalPlan) - 类 中的静态方法org.apache.spark.sql.dynamicpruning.PartitionPruning
-
- apply(SparkPlan) - 类 中的方法org.apache.spark.sql.dynamicpruning.PlanDynamicPruningFilters
-
- apply(Column...) - 类 中的方法org.apache.spark.sql.expressions.UserDefinedAggregateFunction
-
Creates a Column for this UDAF using given Columns as input arguments.
- apply(Seq<Column>) - 类 中的方法org.apache.spark.sql.expressions.UserDefinedAggregateFunction
-
Creates a Column for this UDAF using given Columns as input arguments.
- apply(Column...) - 类 中的方法org.apache.spark.sql.expressions.UserDefinedFunction
-
Returns an expression that invokes the UDF, using the given arguments.
- apply(Seq<Column>) - 类 中的方法org.apache.spark.sql.expressions.UserDefinedFunction
-
Returns an expression that invokes the UDF, using the given arguments.
- apply(LogicalPlan) - 类 中的方法org.apache.spark.sql.hive.DetermineTableStats
-
- apply(T1, T2, T3, T4) - 类 中的静态方法org.apache.spark.sql.hive.execution.CreateHiveTableAsSelectCommand
-
- apply(ScriptInputOutputSchema) - 类 中的静态方法org.apache.spark.sql.hive.execution.HiveScriptIOSchema
-
- apply(T1, T2, T3, T4, T5) - 类 中的静态方法org.apache.spark.sql.hive.execution.InsertIntoHiveDirCommand
-
- apply(T1, T2, T3, T4, T5, T6) - 类 中的静态方法org.apache.spark.sql.hive.execution.InsertIntoHiveTable
-
- apply(T1, T2, T3, T4) - 类 中的静态方法org.apache.spark.sql.hive.execution.OptimizedCreateHiveTableAsSelectCommand
-
- apply(T1, T2, T3, T4, T5) - 类 中的静态方法org.apache.spark.sql.hive.execution.ScriptTransformationExec
-
- apply(LogicalPlan) - 类 中的静态方法org.apache.spark.sql.hive.HiveAnalysis
-
- apply(LogicalPlan) - 类 中的方法org.apache.spark.sql.hive.HiveStrategies.HiveTableScans$
-
- apply(LogicalPlan) - 类 中的静态方法org.apache.spark.sql.hive.HiveStrategies.HiveTableScans
-
- apply(LogicalPlan) - 类 中的方法org.apache.spark.sql.hive.HiveStrategies.Scripts$
-
- apply(LogicalPlan) - 类 中的静态方法org.apache.spark.sql.hive.HiveStrategies.Scripts
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.sql.hive.HiveUDAFBuffer
-
- apply(LogicalPlan) - 类 中的方法org.apache.spark.sql.hive.RelationConversions
-
- apply(LogicalPlan) - 类 中的方法org.apache.spark.sql.hive.ResolveHiveSerdeTable
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.sql.jdbc.JdbcType
-
- apply(Dataset<Row>, Seq<Expression>, RelationalGroupedDataset.GroupType) - 类 中的静态方法org.apache.spark.sql.RelationalGroupedDataset
-
- apply(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i.
- apply(T1, T2) - 类 中的静态方法org.apache.spark.sql.sources.And
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.sql.sources.EqualNullSafe
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.sql.sources.EqualTo
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.sql.sources.GreaterThan
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.sql.sources.GreaterThanOrEqual
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.sql.sources.In
-
- apply(T1) - 类 中的静态方法org.apache.spark.sql.sources.IsNotNull
-
- apply(T1) - 类 中的静态方法org.apache.spark.sql.sources.IsNull
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.sql.sources.LessThan
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.sql.sources.LessThanOrEqual
-
- apply(T1) - 类 中的静态方法org.apache.spark.sql.sources.Not
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.sql.sources.Or
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.sql.sources.StringContains
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.sql.sources.StringEndsWith
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.sql.sources.StringStartsWith
-
- apply(String, Option<Object>) - 类 中的静态方法org.apache.spark.sql.streaming.SinkProgress
-
- apply(DataType) - 类 中的静态方法org.apache.spark.sql.types.ArrayType
-
Construct a
ArrayType object with the given element type.
- apply(T1) - 类 中的静态方法org.apache.spark.sql.types.CharType
-
- apply(double) - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- apply(long) - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- apply(int) - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- apply(BigDecimal) - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- apply(BigDecimal) - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- apply(BigInteger) - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- apply(BigInt) - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- apply(BigDecimal, int, int) - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- apply(BigDecimal, int, int) - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- apply(long, int, int) - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- apply(String) - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- apply(DataType, DataType) - 类 中的静态方法org.apache.spark.sql.types.MapType
-
Construct a
MapType object with the given key type and value type.
- apply(T1, T2, T3, T4) - 类 中的静态方法org.apache.spark.sql.types.StructField
-
- apply(String) - 类 中的方法org.apache.spark.sql.types.StructType
-
- apply(Set<String>) - 类 中的方法org.apache.spark.sql.types.StructType
-
Returns a
StructType containing
StructFields of the given names, preserving the
original order of fields.
- apply(int) - 类 中的方法org.apache.spark.sql.types.StructType
-
- apply(T1) - 类 中的静态方法org.apache.spark.sql.types.VarcharType
-
- apply(T1, T2, T3, T4, T5, T6, T7, T8) - 类 中的静态方法org.apache.spark.status.api.v1.ApplicationAttemptInfo
-
- apply(T1, T2, T3, T4, T5, T6, T7) - 类 中的静态方法org.apache.spark.status.api.v1.ApplicationInfo
-
- apply(T1) - 类 中的静态方法org.apache.spark.status.api.v1.StackTrace
-
- apply(T1, T2, T3, T4, T5, T6, T7) - 类 中的静态方法org.apache.spark.status.api.v1.ThreadStackTrace
-
- apply(int) - 类 中的方法org.apache.spark.status.RDDPartitionSeq
-
- apply(String) - 类 中的静态方法org.apache.spark.storage.BlockId
-
- apply(String, String, int, Option<String>) - 类 中的静态方法org.apache.spark.storage.BlockManagerId
-
- apply(ObjectInput) - 类 中的静态方法org.apache.spark.storage.BlockManagerId
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.storage.BroadcastBlockId
-
- apply(T1, T2) - 类 中的静态方法org.apache.spark.storage.RDDBlockId
-
- apply(T1, T2, T3, T4) - 类 中的静态方法org.apache.spark.storage.ShuffleBlockBatchId
-
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.storage.ShuffleBlockId
-
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.storage.ShuffleDataBlockId
-
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.storage.ShuffleIndexBlockId
-
- apply(boolean, boolean, boolean, boolean, int) - 类 中的静态方法org.apache.spark.storage.StorageLevel
-
:: DeveloperApi ::
Create a new StorageLevel object.
- apply(boolean, boolean, boolean, int) - 类 中的静态方法org.apache.spark.storage.StorageLevel
-
:: DeveloperApi ::
Create a new StorageLevel object without setting useOffHeap.
- apply(int, int) - 类 中的静态方法org.apache.spark.storage.StorageLevel
-
:: DeveloperApi ::
Create a new StorageLevel object from its integer representation.
- apply(ObjectInput) - 类 中的静态方法org.apache.spark.storage.StorageLevel
-
:: DeveloperApi ::
Read StorageLevel object from ObjectInput stream.
- apply(T1, T2) - 类 中的静态方法org.apache.spark.storage.StreamBlockId
-
- apply(T1) - 类 中的静态方法org.apache.spark.storage.TaskResultBlockId
-
- apply(T1) - 类 中的静态方法org.apache.spark.streaming.Duration
-
- apply(long) - 类 中的静态方法org.apache.spark.streaming.Milliseconds
-
- apply(long) - 类 中的静态方法org.apache.spark.streaming.Minutes
-
- apply(T1, T2, T3, T4, T5, T6) - 类 中的静态方法org.apache.spark.streaming.scheduler.BatchInfo
-
- apply(T1, T2, T3, T4, T5, T6, T7) - 类 中的静态方法org.apache.spark.streaming.scheduler.OutputOperationInfo
-
- apply(T1, T2, T3, T4, T5, T6, T7, T8) - 类 中的静态方法org.apache.spark.streaming.scheduler.ReceiverInfo
-
- apply(int) - 类 中的静态方法org.apache.spark.streaming.scheduler.ReceiverState
-
- apply(T1) - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerBatchCompleted
-
- apply(T1) - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerBatchStarted
-
- apply(T1) - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerBatchSubmitted
-
- apply(T1) - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerOutputOperationCompleted
-
- apply(T1) - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerOutputOperationStarted
-
- apply(T1) - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerReceiverError
-
- apply(T1) - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerReceiverStarted
-
- apply(T1) - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerReceiverStopped
-
- apply(T1) - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerStreamingStarted
-
- apply(long) - 类 中的静态方法org.apache.spark.streaming.Seconds
-
- apply(T1, T2, T3) - 类 中的静态方法org.apache.spark.TaskCommitDenied
-
- apply(T1, T2, T3, T4) - 类 中的静态方法org.apache.spark.TaskKilled
-
- apply(int) - 类 中的静态方法org.apache.spark.TaskState
-
- apply(TraversableOnce<Object>) - 类 中的静态方法org.apache.spark.util.StatCounter
-
Build a StatCounter from a list of values.
- apply(Seq<Object>) - 类 中的静态方法org.apache.spark.util.StatCounter
-
Build a StatCounter from a list of values passed as variable-length arguments.
- APPLY_CUSTOM_EXECUTOR_LOG_URL_TO_INCOMPLETE_APP() - 类 中的静态方法org.apache.spark.internal.config.History
-
- ApplyInPlace - org.apache.spark.ml.ann中的类
-
Implements in-place application of functions in the arrays
- ApplyInPlace() - 类 的构造器org.apache.spark.ml.ann.ApplyInPlace
-
- applyNamespaceChanges(Map<String, String>, Seq<NamespaceChange>) - 类 中的静态方法org.apache.spark.sql.connector.catalog.CatalogV2Util
-
Apply properties changes to a map and return the result.
- applyNamespaceChanges(Map<String, String>, Seq<NamespaceChange>) - 类 中的静态方法org.apache.spark.sql.connector.catalog.CatalogV2Util
-
Apply properties changes to a Java map and return the result.
- applyPropertiesChanges(Map<String, String>, Seq<TableChange>) - 类 中的静态方法org.apache.spark.sql.connector.catalog.CatalogV2Util
-
Apply properties changes to a map and return the result.
- applyPropertiesChanges(Map<String, String>, Seq<TableChange>) - 类 中的静态方法org.apache.spark.sql.connector.catalog.CatalogV2Util
-
Apply properties changes to a Java map and return the result.
- applySchemaChanges(StructType, Seq<TableChange>) - 类 中的静态方法org.apache.spark.sql.connector.catalog.CatalogV2Util
-
Apply schema changes to a schema and return the result.
- appName() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
- appName() - 类 中的方法org.apache.spark.scheduler.SparkListenerApplicationStart
-
- appName() - 类 中的方法org.apache.spark.SparkContext
-
- appName(String) - 类 中的方法org.apache.spark.sql.SparkSession.Builder
-
Sets a name for the application, which will be shown in the Spark web UI.
- approx_count_distinct(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the approximate number of distinct items in a group.
- approx_count_distinct(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the approximate number of distinct items in a group.
- approx_count_distinct(Column, double) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the approximate number of distinct items in a group.
- approx_count_distinct(String, double) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the approximate number of distinct items in a group.
- ApproxHist() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.QuantileStrategy
-
- ApproximateEvaluator<U,R> - org.apache.spark.partial中的接口
-
An object that computes a function incrementally by merging in results of type U from multiple
tasks.
- approxQuantile(String, double[], double) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
Calculates the approximate quantiles of a numerical column of a DataFrame.
- approxQuantile(String[], double[], double) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
Calculates the approximate quantiles of numerical columns of a DataFrame.
- appSparkVersion() - 类 中的方法org.apache.spark.status.api.v1.ApplicationAttemptInfo
-
- AppStatusUtils - org.apache.spark.status中的类
-
- AppStatusUtils() - 类 的构造器org.apache.spark.status.AppStatusUtils
-
- AreaUnderCurve - org.apache.spark.mllib.evaluation中的类
-
Computes the area under the curve (AUC) using the trapezoidal rule.
- AreaUnderCurve() - 类 的构造器org.apache.spark.mllib.evaluation.AreaUnderCurve
-
- areaUnderPR() - 类 中的方法org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
-
Computes the area under the precision-recall curve.
- areaUnderROC() - 接口 中的方法org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
-
Computes the area under the receiver operating characteristic (ROC) curve.
- areaUnderROC() - 类 中的方法org.apache.spark.ml.classification.BinaryLogisticRegressionSummaryImpl
-
- areaUnderROC() - 类 中的方法org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
-
Computes the area under the receiver operating characteristic (ROC) curve.
- argmax() - 类 中的方法org.apache.spark.ml.linalg.DenseVector
-
- argmax() - 类 中的方法org.apache.spark.ml.linalg.SparseVector
-
- argmax() - 接口 中的方法org.apache.spark.ml.linalg.Vector
-
Find the index of a maximal element.
- argmax() - 类 中的方法org.apache.spark.mllib.linalg.DenseVector
-
- argmax() - 类 中的方法org.apache.spark.mllib.linalg.SparseVector
-
- argmax() - 接口 中的方法org.apache.spark.mllib.linalg.Vector
-
Find the index of a maximal element.
- argString(int) - 接口 中的方法org.apache.spark.sql.hive.execution.CreateHiveTableAsSelectBase
-
- arguments() - 接口 中的方法org.apache.spark.sql.connector.expressions.Transform
-
Returns the arguments passed to the transform function.
- array(DataType) - 类 中的方法org.apache.spark.sql.ColumnName
-
Creates a new StructField of type array.
- array(Column...) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a new array column.
- array(String, String...) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a new array column.
- array(Seq<Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a new array column.
- array(String, Seq<String>) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a new array column.
- array() - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- array_contains(Column, Object) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns null if the array is null, true if the array contains value, and false otherwise.
- array_distinct(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Removes duplicate values from the array.
- array_except(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns an array of the elements in the first array but not in the second array,
without duplicates.
- array_intersect(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns an array of the elements in the intersection of the given two arrays,
without duplicates.
- array_join(Column, String, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Concatenates the elements of column using the delimiter.
- array_join(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Concatenates the elements of column using the delimiter.
- array_max(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the maximum value in the array.
- array_min(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the minimum value in the array.
- array_position(Column, Object) - 类 中的静态方法org.apache.spark.sql.functions
-
Locates the position of the first occurrence of the value in the given array as long.
- array_remove(Column, Object) - 类 中的静态方法org.apache.spark.sql.functions
-
Remove all elements that equal to element from the given array.
- array_repeat(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates an array containing the left argument repeated the number of times given by the
right argument.
- array_repeat(Column, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates an array containing the left argument repeated the number of times given by the
right argument.
- array_sort(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Sorts the input array in ascending order.
- array_union(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns an array of the elements in the union of the given two arrays, without duplicates.
- arrayLengthGt(double) - 类 中的静态方法org.apache.spark.ml.param.ParamValidators
-
Check that the array length is greater than lowerBound.
- arrays_overlap(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns true if a1 and a2 have at least one non-null element in common.
- arrays_zip(Column...) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns a merged array of structs in which the N-th struct contains all N-th values of input
arrays.
- arrays_zip(Seq<Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns a merged array of structs in which the N-th struct contains all N-th values of input
arrays.
- ArrayType - org.apache.spark.sql.types中的类
-
- ArrayType(DataType, boolean) - 类 的构造器org.apache.spark.sql.types.ArrayType
-
- arrayValues() - 类 中的方法org.apache.spark.storage.memory.DeserializedValuesHolder
-
- ArrowColumnVector - org.apache.spark.sql.vectorized中的类
-
A column vector backed by Apache Arrow.
- ArrowColumnVector(ValueVector) - 类 的构造器org.apache.spark.sql.vectorized.ArrowColumnVector
-
- ArrowUtils - org.apache.spark.sql.util中的类
-
- ArrowUtils() - 类 的构造器org.apache.spark.sql.util.ArrowUtils
-
- as(Encoder<U>) - 类 中的方法org.apache.spark.sql.Column
-
Provides a type hint about the expected return value of this column.
- as(String) - 类 中的方法org.apache.spark.sql.Column
-
Gives the column an alias.
- as(Seq<String>) - 类 中的方法org.apache.spark.sql.Column
-
(Scala-specific) Assigns the given aliases to the results of a table generating function.
- as(String[]) - 类 中的方法org.apache.spark.sql.Column
-
Assigns the given aliases to the results of a table generating function.
- as(Symbol) - 类 中的方法org.apache.spark.sql.Column
-
Gives the column an alias.
- as(String, Metadata) - 类 中的方法org.apache.spark.sql.Column
-
Gives the column an alias with metadata.
- as(Encoder<U>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset where each record has been mapped on to the specified type.
- as(String) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset with an alias set.
- as(Symbol) - 类 中的方法org.apache.spark.sql.Dataset
-
(Scala-specific) Returns a new Dataset with an alias set.
- asBinary() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionSummary
-
Convenient method for casting to binary logistic regression summary.
- asBreeze() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Converts to a breeze matrix.
- asBreeze() - 接口 中的方法org.apache.spark.ml.linalg.Vector
-
Converts the instance to a breeze vector.
- asBreeze() - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
Converts to a breeze matrix.
- asBreeze() - 接口 中的方法org.apache.spark.mllib.linalg.Vector
-
Converts the instance to a breeze vector.
- asc() - 类 中的方法org.apache.spark.sql.Column
-
Returns a sort expression based on ascending order of the column.
- asc(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns a sort expression based on ascending order of the column.
- asc_nulls_first() - 类 中的方法org.apache.spark.sql.Column
-
Returns a sort expression based on ascending order of the column,
and null values return before non-null values.
- asc_nulls_first(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns a sort expression based on ascending order of the column,
and null values return before non-null values.
- asc_nulls_last() - 类 中的方法org.apache.spark.sql.Column
-
Returns a sort expression based on ascending order of the column,
and null values appear after non-null values.
- asc_nulls_last(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns a sort expression based on ascending order of the column,
and null values appear after non-null values.
- asCaseSensitiveMap() - 类 中的方法org.apache.spark.sql.util.CaseInsensitiveStringMap
-
Returns the original case-sensitive map.
- ascii(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the numeric value of the first character of the string column, and returns the
result as an int column.
- asIdentifier() - 类 中的方法org.apache.spark.sql.connector.catalog.CatalogV2Implicits.MultipartIdentifierHelper
-
- asin(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
- asin(String) - 类 中的静态方法org.apache.spark.sql.functions
-
- asInteraction() - 类 中的静态方法org.apache.spark.ml.feature.Dot
-
- asInteraction() - 接口 中的方法org.apache.spark.ml.feature.InteractableTerm
-
Convert to ColumnInteraction to wrap all interactions.
- asIterator() - 类 中的方法org.apache.spark.serializer.DeserializationStream
-
Read the elements of this stream through an iterator.
- asJavaPairRDD() - 类 中的方法org.apache.spark.api.r.PairwiseRRDD
-
- asJavaRDD() - 类 中的方法org.apache.spark.api.r.RRDD
-
- asJavaRDD() - 类 中的方法org.apache.spark.api.r.StringRRDD
-
- asKeyValueIterator() - 类 中的方法org.apache.spark.serializer.DeserializationStream
-
Read the elements of this stream through an iterator over key-value pairs.
- AskPermissionToCommitOutput - org.apache.spark.scheduler中的类
-
- AskPermissionToCommitOutput(int, int, int, int) - 类 的构造器org.apache.spark.scheduler.AskPermissionToCommitOutput
-
- askRpcTimeout(SparkConf) - 类 中的静态方法org.apache.spark.util.RpcUtils
-
Returns the default Spark timeout to use for RPC ask operations.
- askSlaves() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.GetBlockStatus
-
- askSlaves() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.GetMatchingBlockIds
-
- asML() - 类 中的方法org.apache.spark.mllib.linalg.DenseMatrix
-
- asML() - 类 中的方法org.apache.spark.mllib.linalg.DenseVector
-
- asML() - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
Convert this matrix to the new mllib-local representation.
- asML() - 类 中的方法org.apache.spark.mllib.linalg.SparseMatrix
-
- asML() - 类 中的方法org.apache.spark.mllib.linalg.SparseVector
-
- asML() - 接口 中的方法org.apache.spark.mllib.linalg.Vector
-
Convert this vector to the new mllib-local representation.
- asNamespaceCatalog() - 类 中的方法org.apache.spark.sql.connector.catalog.CatalogV2Implicits.CatalogHelper
-
- asNondeterministic() - 类 中的方法org.apache.spark.sql.expressions.UserDefinedFunction
-
Updates UserDefinedFunction to nondeterministic.
- asNonNullable() - 类 中的方法org.apache.spark.sql.expressions.UserDefinedFunction
-
Updates UserDefinedFunction to non-nullable.
- asNullable() - 类 中的方法org.apache.spark.sql.types.ObjectType
-
- asPartitionColumns() - 类 中的方法org.apache.spark.sql.connector.catalog.CatalogV2Implicits.TransformHelper
-
- asRDDId() - 类 中的方法org.apache.spark.storage.BlockId
-
- assertConf(JobContext, SparkConf) - 类 中的方法org.apache.spark.internal.io.HadoopWriteConfigUtil
-
- assertExceptionMsg(Throwable, String) - 类 中的静态方法org.apache.spark.TestUtils
-
Asserts that exception message contains the message.
- assertNotSpilled(SparkContext, String, Function0<BoxedUnit>) - 类 中的静态方法org.apache.spark.TestUtils
-
Run some code involving jobs submitted to the given context and assert that the jobs
did not spill.
- assertSpilled(SparkContext, String, Function0<BoxedUnit>) - 类 中的静态方法org.apache.spark.TestUtils
-
Run some code involving jobs submitted to the given context and assert that the jobs spilled.
- assignClusters(Dataset<?>) - 类 中的方法org.apache.spark.ml.clustering.PowerIterationClustering
-
Run the PIC algorithm and returns a cluster assignment for each input vertex.
- assignedAddrs() - 接口 中的方法org.apache.spark.resource.ResourceAllocator
-
Sequence of currently assigned resource addresses.
- Assignment(long, int) - 类 的构造器org.apache.spark.mllib.clustering.PowerIterationClustering.Assignment
-
- Assignment$() - 类 的构造器org.apache.spark.mllib.clustering.PowerIterationClustering.Assignment$
-
- assignments() - 类 中的方法org.apache.spark.mllib.clustering.PowerIterationClusteringModel
-
- AssociationRules - org.apache.spark.ml.fpm中的类
-
- AssociationRules() - 类 的构造器org.apache.spark.ml.fpm.AssociationRules
-
- associationRules() - 类 中的方法org.apache.spark.ml.fpm.FPGrowthModel
-
Get association rules fitted using the minConfidence.
- AssociationRules - org.apache.spark.mllib.fpm中的类
-
Generates association rules from a RDD[FreqItemset[Item}.
- AssociationRules() - 类 的构造器org.apache.spark.mllib.fpm.AssociationRules
-
Constructs a default instance with default parameters {minConfidence = 0.8}.
- AssociationRules.Rule<Item> - org.apache.spark.mllib.fpm中的类
-
An association rule between sets of items.
- asTableCatalog() - 类 中的方法org.apache.spark.sql.connector.catalog.CatalogV2Implicits.CatalogHelper
-
- asTableIdentifier() - 类 中的方法org.apache.spark.sql.connector.catalog.CatalogV2Implicits.MultipartIdentifierHelper
-
- AsTableIdentifier() - 接口 中的方法org.apache.spark.sql.connector.catalog.LookupCatalog
-
- AsTableIdentifier() - 类 的构造器org.apache.spark.sql.connector.catalog.LookupCatalog.AsTableIdentifier
-
- AsTableIdentifier$() - 类 的构造器org.apache.spark.sql.connector.catalog.LookupCatalog.AsTableIdentifier$
-
- AsTemporaryViewIdentifier() - 接口 中的方法org.apache.spark.sql.connector.catalog.LookupCatalog
-
- AsTemporaryViewIdentifier() - 类 的构造器org.apache.spark.sql.connector.catalog.LookupCatalog.AsTemporaryViewIdentifier
-
- AsTemporaryViewIdentifier$() - 类 的构造器org.apache.spark.sql.connector.catalog.LookupCatalog.AsTemporaryViewIdentifier$
-
- asTerms() - 类 中的静态方法org.apache.spark.ml.feature.Dot
-
- asTerms() - 类 中的静态方法org.apache.spark.ml.feature.EmptyTerm
-
- asTerms() - 接口 中的方法org.apache.spark.ml.feature.Term
-
Default representation of a single Term as a part of summed terms.
- asTransform() - 类 中的方法org.apache.spark.sql.connector.catalog.CatalogV2Implicits.BucketSpecHelper
-
- asTransforms() - 类 中的方法org.apache.spark.sql.connector.catalog.CatalogV2Implicits.PartitionTypeHelper
-
- ASYNC_TRACKING_ENABLED() - 类 中的静态方法org.apache.spark.internal.config.Status
-
- AsyncEventQueue - org.apache.spark.scheduler中的类
-
An asynchronous queue for events.
- AsyncEventQueue(String, SparkConf, LiveListenerBusMetrics, LiveListenerBus) - 类 的构造器org.apache.spark.scheduler.AsyncEventQueue
-
- AsyncRDDActions<T> - org.apache.spark.rdd中的类
-
A set of asynchronous RDD actions available through an implicit conversion.
- AsyncRDDActions(RDD<T>, ClassTag<T>) - 类 的构造器org.apache.spark.rdd.AsyncRDDActions
-
- atan(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
- atan(String) - 类 中的静态方法org.apache.spark.sql.functions
-
- atan2(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
- atan2(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
- atan2(String, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
- atan2(String, String) - 类 中的静态方法org.apache.spark.sql.functions
-
- atan2(Column, double) - 类 中的静态方法org.apache.spark.sql.functions
-
- atan2(String, double) - 类 中的静态方法org.apache.spark.sql.functions
-
- atan2(double, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
- atan2(double, String) - 类 中的静态方法org.apache.spark.sql.functions
-
- attempt() - 类 中的方法org.apache.spark.status.api.v1.TaskData
-
- ATTEMPT() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- attemptId() - 类 中的方法org.apache.spark.status.api.v1.ApplicationAttemptInfo
-
- attemptId() - 接口 中的方法org.apache.spark.status.api.v1.BaseAppResource
-
- attemptId() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- attemptNumber() - 类 中的方法org.apache.spark.BarrierTaskContext
-
- attemptNumber() - 类 中的方法org.apache.spark.scheduler.AskPermissionToCommitOutput
-
- attemptNumber() - 类 中的方法org.apache.spark.scheduler.StageInfo
-
- attemptNumber() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
- attemptNumber() - 类 中的方法org.apache.spark.TaskCommitDenied
-
- attemptNumber() - 类 中的方法org.apache.spark.TaskContext
-
How many times this task has been attempted.
- attempts() - 类 中的方法org.apache.spark.status.api.v1.ApplicationInfo
-
- AtTimestamp(Date) - 类 的构造器org.apache.spark.streaming.kinesis.KinesisInitialPositions.AtTimestamp
-
- attr() - 类 中的方法org.apache.spark.graphx.Edge
-
- attr() - 类 中的方法org.apache.spark.graphx.EdgeContext
-
The attribute associated with the edge.
- attr() - 类 中的方法org.apache.spark.graphx.impl.AggregatingEdgeContext
-
- Attribute - org.apache.spark.ml.attribute中的类
-
:: DeveloperApi ::
Abstract class for ML attributes.
- Attribute() - 类 的构造器org.apache.spark.ml.attribute.Attribute
-
- attribute() - 类 中的方法org.apache.spark.sql.sources.EqualNullSafe
-
- attribute() - 类 中的方法org.apache.spark.sql.sources.EqualTo
-
- attribute() - 类 中的方法org.apache.spark.sql.sources.GreaterThan
-
- attribute() - 类 中的方法org.apache.spark.sql.sources.GreaterThanOrEqual
-
- attribute() - 类 中的方法org.apache.spark.sql.sources.In
-
- attribute() - 类 中的方法org.apache.spark.sql.sources.IsNotNull
-
- attribute() - 类 中的方法org.apache.spark.sql.sources.IsNull
-
- attribute() - 类 中的方法org.apache.spark.sql.sources.LessThan
-
- attribute() - 类 中的方法org.apache.spark.sql.sources.LessThanOrEqual
-
- attribute() - 类 中的方法org.apache.spark.sql.sources.StringContains
-
- attribute() - 类 中的方法org.apache.spark.sql.sources.StringEndsWith
-
- attribute() - 类 中的方法org.apache.spark.sql.sources.StringStartsWith
-
- AttributeFactory - org.apache.spark.ml.attribute中的接口
-
Trait for ML attribute factories.
- AttributeGroup - org.apache.spark.ml.attribute中的类
-
:: DeveloperApi ::
Attributes that describe a vector ML column.
- AttributeGroup(String) - 类 的构造器org.apache.spark.ml.attribute.AttributeGroup
-
Creates an attribute group without attribute info.
- AttributeGroup(String, int) - 类 的构造器org.apache.spark.ml.attribute.AttributeGroup
-
Creates an attribute group knowing only the number of attributes.
- AttributeGroup(String, Attribute[]) - 类 的构造器org.apache.spark.ml.attribute.AttributeGroup
-
Creates an attribute group with attributes.
- AttributeKeys - org.apache.spark.ml.attribute中的类
-
Keys used to store attributes.
- AttributeKeys() - 类 的构造器org.apache.spark.ml.attribute.AttributeKeys
-
- attributes() - 类 中的方法org.apache.spark.ml.attribute.AttributeGroup
-
Optional array of attributes.
- ATTRIBUTES() - 类 中的静态方法org.apache.spark.ml.attribute.AttributeKeys
-
- attributes() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterExecutor
-
- attributes() - 类 中的方法org.apache.spark.scheduler.cluster.ExecutorInfo
-
- attributes() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- attributes() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- AttributeType - org.apache.spark.ml.attribute中的类
-
:: DeveloperApi ::
An enum-like type for attribute types: AttributeType$.Numeric, AttributeType$.Nominal,
and AttributeType$.Binary.
- AttributeType(String) - 类 的构造器org.apache.spark.ml.attribute.AttributeType
-
- attrType() - 类 中的方法org.apache.spark.ml.attribute.Attribute
-
Attribute type.
- attrType() - 类 中的方法org.apache.spark.ml.attribute.BinaryAttribute
-
- attrType() - 类 中的方法org.apache.spark.ml.attribute.NominalAttribute
-
- attrType() - 类 中的方法org.apache.spark.ml.attribute.NumericAttribute
-
- attrType() - 类 中的静态方法org.apache.spark.ml.attribute.UnresolvedAttribute
-
- available() - 类 中的方法org.apache.spark.io.NioBufferedFileInputStream
-
- available() - 类 中的方法org.apache.spark.io.ReadAheadInputStream
-
- available() - 类 中的方法org.apache.spark.storage.BufferReleasingInputStream
-
- availableAddrs() - 接口 中的方法org.apache.spark.resource.ResourceAllocator
-
Sequence of currently available resource addresses.
- Average() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.EnsembleCombiningStrategy
-
- avg(MapFunction<T, Double>) - 类 中的静态方法org.apache.spark.sql.expressions.javalang.typed
-
已过时。
Average aggregate function.
- avg(Function1<IN, Object>) - 类 中的静态方法org.apache.spark.sql.expressions.scalalang.typed
-
已过时。
Average aggregate function.
- avg(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the average of the values in a group.
- avg(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the average of the values in a group.
- avg(String...) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Compute the mean value for each numeric columns for each group.
- avg(Seq<String>) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Compute the mean value for each numeric columns for each group.
- avg() - 类 中的方法org.apache.spark.util.DoubleAccumulator
-
Returns the average of elements added to the accumulator.
- avg() - 类 中的方法org.apache.spark.util.LongAccumulator
-
Returns the average of elements added to the accumulator.
- avgEventRate() - 类 中的方法org.apache.spark.status.api.v1.streaming.ReceiverInfo
-
- avgInputRate() - 类 中的方法org.apache.spark.status.api.v1.streaming.StreamingStatistics
-
- avgMetrics() - 类 中的方法org.apache.spark.ml.tuning.CrossValidatorModel
-
- avgProcessingTime() - 类 中的方法org.apache.spark.status.api.v1.streaming.StreamingStatistics
-
- avgSchedulingDelay() - 类 中的方法org.apache.spark.status.api.v1.streaming.StreamingStatistics
-
- avgTotalDelay() - 类 中的方法org.apache.spark.status.api.v1.streaming.StreamingStatistics
-
- awaitAnyTermination() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryManager
-
Wait until any of the queries on the associated SQLContext has terminated since the
creation of the context, or since resetTerminated() was called.
- awaitAnyTermination(long) - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryManager
-
Wait until any of the queries on the associated SQLContext has terminated since the
creation of the context, or since resetTerminated() was called.
- awaitReady(Awaitable<T>, Duration) - 类 中的静态方法org.apache.spark.util.ThreadUtils
-
Preferred alternative to Await.ready().
- awaitResult(Awaitable<T>, Duration) - 类 中的静态方法org.apache.spark.util.ThreadUtils
-
Preferred alternative to Await.result().
- awaitTermination() - 接口 中的方法org.apache.spark.sql.streaming.StreamingQuery
-
Waits for the termination of this query, either by query.stop() or by an exception.
- awaitTermination(long) - 接口 中的方法org.apache.spark.sql.streaming.StreamingQuery
-
Waits for the termination of this query, either by query.stop() or by an exception.
- awaitTermination() - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Wait for the execution to stop.
- awaitTermination() - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Wait for the execution to stop.
- awaitTerminationOrTimeout(long) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Wait for the execution to stop.
- awaitTerminationOrTimeout(long) - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Wait for the execution to stop.
- axpy(double, Vector, Vector) - 类 中的静态方法org.apache.spark.ml.linalg.BLAS
-
y += a * x
- axpy(double, Vector, Vector) - 类 中的静态方法org.apache.spark.mllib.linalg.BLAS
-
y += a * x
- cache() - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Persist this RDD with the default storage level (MEMORY_ONLY).
- cache() - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Persist this RDD with the default storage level (MEMORY_ONLY).
- cache() - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Persist this RDD with the default storage level (MEMORY_ONLY).
- cache() - 类 中的方法org.apache.spark.graphx.Graph
-
Caches the vertices and edges associated with this graph at the previously-specified target
storage levels, which default to MEMORY_ONLY.
- cache() - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
Persists the edge partitions using targetStorageLevel, which defaults to MEMORY_ONLY.
- cache() - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- cache() - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
Persists the vertex partitions at targetStorageLevel, which defaults to MEMORY_ONLY.
- cache() - 类 中的方法org.apache.spark.mllib.linalg.distributed.BlockMatrix
-
Caches the underlying RDD.
- cache() - 类 中的方法org.apache.spark.rdd.RDD
-
Persist this RDD with the default storage level (MEMORY_ONLY).
- cache() - 类 中的方法org.apache.spark.sql.Dataset
-
Persist this Dataset with the default storage level (MEMORY_AND_DISK).
- cache() - 类 中的方法org.apache.spark.streaming.api.java.JavaDStream
-
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
- cache() - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
- cache() - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
- CACHED_PARTITIONS() - 类 中的静态方法org.apache.spark.ui.storage.ToolTips
-
- cacheNodeIds() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- cacheNodeIds() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- cacheNodeIds() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- cacheNodeIds() - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- cacheNodeIds() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- cacheNodeIds() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- cacheNodeIds() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- cacheNodeIds() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- cacheNodeIds() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- cacheNodeIds() - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- cacheNodeIds() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- cacheNodeIds() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- cacheNodeIds() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
If false, the algorithm will pass trees to executors to match instances with nodes.
- cacheSize() - 接口 中的方法org.apache.spark.SparkExecutorInfo
-
- cacheSize() - 类 中的方法org.apache.spark.SparkExecutorInfoImpl
-
- cacheTable(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Caches the specified table in-memory.
- cacheTable(String, StorageLevel) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Caches the specified table with the given storage level.
- cacheTable(String) - 类 中的方法org.apache.spark.sql.SQLContext
-
Caches the specified table in-memory.
- calculate(DenseVector<Object>) - 类 中的方法org.apache.spark.ml.regression.AFTCostFun
-
- calculate(double[], double) - 类 中的静态方法org.apache.spark.mllib.tree.impurity.Entropy
-
:: DeveloperApi ::
information calculation for multiclass classification
- calculate(double, double, double) - 类 中的静态方法org.apache.spark.mllib.tree.impurity.Entropy
-
:: DeveloperApi ::
variance calculation
- calculate(double[], double) - 类 中的静态方法org.apache.spark.mllib.tree.impurity.Gini
-
:: DeveloperApi ::
information calculation for multiclass classification
- calculate(double, double, double) - 类 中的静态方法org.apache.spark.mllib.tree.impurity.Gini
-
:: DeveloperApi ::
variance calculation
- calculate(double[], double) - 接口 中的方法org.apache.spark.mllib.tree.impurity.Impurity
-
:: DeveloperApi ::
information calculation for multiclass classification
- calculate(double, double, double) - 接口 中的方法org.apache.spark.mllib.tree.impurity.Impurity
-
:: DeveloperApi ::
information calculation for regression
- calculate(double[], double) - 类 中的静态方法org.apache.spark.mllib.tree.impurity.Variance
-
:: DeveloperApi ::
information calculation for multiclass classification
- calculate(double, double, double) - 类 中的静态方法org.apache.spark.mllib.tree.impurity.Variance
-
:: DeveloperApi ::
variance calculation
- calculateNumberOfPartitions(long, int, int) - 类 中的方法org.apache.spark.ml.feature.Word2VecModel.Word2VecModelWriter$
-
Calculate the number of partitions to use in saving the model.
- CalendarIntervalType - org.apache.spark.sql.types中的类
-
The data type representing calendar time intervals.
- CalendarIntervalType() - 类 的构造器org.apache.spark.sql.types.CalendarIntervalType
-
- CalendarIntervalType - 类 中的静态变量org.apache.spark.sql.types.DataTypes
-
Gets the CalendarIntervalType object.
- call(K, Iterator<V1>, Iterator<V2>) - 接口 中的方法org.apache.spark.api.java.function.CoGroupFunction
-
- call(T) - 接口 中的方法org.apache.spark.api.java.function.DoubleFlatMapFunction
-
- call(T) - 接口 中的方法org.apache.spark.api.java.function.DoubleFunction
-
- call(T) - 接口 中的方法org.apache.spark.api.java.function.FilterFunction
-
- call(T) - 接口 中的方法org.apache.spark.api.java.function.FlatMapFunction
-
- call(T1, T2) - 接口 中的方法org.apache.spark.api.java.function.FlatMapFunction2
-
- call(K, Iterator<V>) - 接口 中的方法org.apache.spark.api.java.function.FlatMapGroupsFunction
-
- call(K, Iterator<V>, GroupState<S>) - 接口 中的方法org.apache.spark.api.java.function.FlatMapGroupsWithStateFunction
-
- call(T) - 接口 中的方法org.apache.spark.api.java.function.ForeachFunction
-
- call(Iterator<T>) - 接口 中的方法org.apache.spark.api.java.function.ForeachPartitionFunction
-
- call(T1) - 接口 中的方法org.apache.spark.api.java.function.Function
-
- call() - 接口 中的方法org.apache.spark.api.java.function.Function0
-
- call(T1, T2) - 接口 中的方法org.apache.spark.api.java.function.Function2
-
- call(T1, T2, T3) - 接口 中的方法org.apache.spark.api.java.function.Function3
-
- call(T1, T2, T3, T4) - 接口 中的方法org.apache.spark.api.java.function.Function4
-
- call(T) - 接口 中的方法org.apache.spark.api.java.function.MapFunction
-
- call(K, Iterator<V>) - 接口 中的方法org.apache.spark.api.java.function.MapGroupsFunction
-
- call(K, Iterator<V>, GroupState<S>) - 接口 中的方法org.apache.spark.api.java.function.MapGroupsWithStateFunction
-
- call(Iterator<T>) - 接口 中的方法org.apache.spark.api.java.function.MapPartitionsFunction
-
- call(T) - 接口 中的方法org.apache.spark.api.java.function.PairFlatMapFunction
-
- call(T) - 接口 中的方法org.apache.spark.api.java.function.PairFunction
-
- call(T, T) - 接口 中的方法org.apache.spark.api.java.function.ReduceFunction
-
- call(T) - 接口 中的方法org.apache.spark.api.java.function.VoidFunction
-
- call(T1, T2) - 接口 中的方法org.apache.spark.api.java.function.VoidFunction2
-
- call() - 接口 中的方法org.apache.spark.sql.api.java.UDF0
-
- call(T1) - 接口 中的方法org.apache.spark.sql.api.java.UDF1
-
- call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10) - 接口 中的方法org.apache.spark.sql.api.java.UDF10
-
- call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11) - 接口 中的方法org.apache.spark.sql.api.java.UDF11
-
- call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12) - 接口 中的方法org.apache.spark.sql.api.java.UDF12
-
- call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13) - 接口 中的方法org.apache.spark.sql.api.java.UDF13
-
- call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14) - 接口 中的方法org.apache.spark.sql.api.java.UDF14
-
- call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15) - 接口 中的方法org.apache.spark.sql.api.java.UDF15
-
- call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15, T16) - 接口 中的方法org.apache.spark.sql.api.java.UDF16
-
- call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15, T16, T17) - 接口 中的方法org.apache.spark.sql.api.java.UDF17
-
- call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15, T16, T17, T18) - 接口 中的方法org.apache.spark.sql.api.java.UDF18
-
- call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15, T16, T17, T18, T19) - 接口 中的方法org.apache.spark.sql.api.java.UDF19
-
- call(T1, T2) - 接口 中的方法org.apache.spark.sql.api.java.UDF2
-
- call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15, T16, T17, T18, T19, T20) - 接口 中的方法org.apache.spark.sql.api.java.UDF20
-
- call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15, T16, T17, T18, T19, T20, T21) - 接口 中的方法org.apache.spark.sql.api.java.UDF21
-
- call(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15, T16, T17, T18, T19, T20, T21, T22) - 接口 中的方法org.apache.spark.sql.api.java.UDF22
-
- call(T1, T2, T3) - 接口 中的方法org.apache.spark.sql.api.java.UDF3
-
- call(T1, T2, T3, T4) - 接口 中的方法org.apache.spark.sql.api.java.UDF4
-
- call(T1, T2, T3, T4, T5) - 接口 中的方法org.apache.spark.sql.api.java.UDF5
-
- call(T1, T2, T3, T4, T5, T6) - 接口 中的方法org.apache.spark.sql.api.java.UDF6
-
- call(T1, T2, T3, T4, T5, T6, T7) - 接口 中的方法org.apache.spark.sql.api.java.UDF7
-
- call(T1, T2, T3, T4, T5, T6, T7, T8) - 接口 中的方法org.apache.spark.sql.api.java.UDF8
-
- call(T1, T2, T3, T4, T5, T6, T7, T8, T9) - 接口 中的方法org.apache.spark.sql.api.java.UDF9
-
- callSite() - 类 中的方法org.apache.spark.storage.RDDInfo
-
- callUDF(String, Column...) - 类 中的静态方法org.apache.spark.sql.functions
-
Call an user-defined function.
- callUDF(String, Seq<Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Call an user-defined function.
- cancel() - 类 中的方法org.apache.spark.ComplexFutureAction
-
- cancel() - 接口 中的方法org.apache.spark.FutureAction
-
Cancels the execution of this action.
- cancel() - 类 中的方法org.apache.spark.SimpleFutureAction
-
- cancelAllJobs() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Cancel all jobs that have been scheduled or are running.
- cancelAllJobs() - 类 中的方法org.apache.spark.SparkContext
-
Cancel all jobs that have been scheduled or are running.
- cancelJob(int, String) - 类 中的方法org.apache.spark.SparkContext
-
Cancel a given job if it's scheduled or running.
- cancelJob(int) - 类 中的方法org.apache.spark.SparkContext
-
Cancel a given job if it's scheduled or running.
- cancelJobGroup(String) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Cancel active jobs for the specified group.
- cancelJobGroup(String) - 类 中的方法org.apache.spark.SparkContext
-
Cancel active jobs for the specified group.
- cancelStage(int, String) - 类 中的方法org.apache.spark.SparkContext
-
Cancel a given stage and all jobs associated with it.
- cancelStage(int) - 类 中的方法org.apache.spark.SparkContext
-
Cancel a given stage and all jobs associated with it.
- cancelTasks(int, boolean) - 接口 中的方法org.apache.spark.scheduler.TaskScheduler
-
- canCreate(String) - 接口 中的方法org.apache.spark.scheduler.ExternalClusterManager
-
Check if this cluster manager instance can create scheduler components
for a certain master URL.
- canDoMerge() - 类 中的方法org.apache.spark.sql.hive.HiveUDAFBuffer
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.ExpireDeadHosts
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.metrics.DirectPoolMemory
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.metrics.GarbageCollectionMetrics
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.metrics.JVMHeapMemory
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.metrics.JVMOffHeapMemory
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.metrics.MappedPoolMemory
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.metrics.OffHeapExecutionMemory
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.metrics.OffHeapStorageMemory
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.metrics.OffHeapUnifiedMemory
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.metrics.OnHeapExecutionMemory
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.metrics.OnHeapStorageMemory
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.metrics.OnHeapUnifiedMemory
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.metrics.ProcessTreeMetrics
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.ml.feature.Dot
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.ml.feature.EmptyTerm
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.Resubmitted
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.rpc.netty.OnStart
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.rpc.netty.OnStop
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.scheduler.AllJobsCancelled
-
- canEqual(Object) - 类 中的方法org.apache.spark.scheduler.cluster.ExecutorInfo
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.scheduler.JobSucceeded
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.scheduler.ResubmitFailedStages
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.scheduler.StopCoordinator
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.jdbc.MySQLDialect
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.jdbc.OracleDialect
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.jdbc.TeradataDialect
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.sources.AlwaysFalse
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.sources.AlwaysTrue
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.types.BinaryType
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.types.BooleanType
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.types.ByteType
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.types.CalendarIntervalType
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.types.DateType
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.types.DoubleType
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.types.FloatType
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.types.IntegerType
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.types.LongType
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.types.NullType
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.types.ShortType
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.types.StringType
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.sql.types.TimestampType
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.StopMapOutputTracker
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.streaming.kinesis.DefaultCredentials
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.streaming.scheduler.AllReceiverIds
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.streaming.scheduler.GetAllReceiverInfo
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.streaming.scheduler.StopAllReceivers
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.Success
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.TaskResultLost
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.TaskSchedulerIsSet
-
- canEqual(Object) - 类 中的静态方法org.apache.spark.UnknownReason
-
- canEqual(Object) - 类 中的方法org.apache.spark.util.MutablePair
-
- canHandle(String) - 类 中的方法org.apache.spark.sql.jdbc.AggregatedDialect
-
- canHandle(String) - 类 中的静态方法org.apache.spark.sql.jdbc.DB2Dialect
-
- canHandle(String) - 类 中的静态方法org.apache.spark.sql.jdbc.DerbyDialect
-
- canHandle(String) - 类 中的方法org.apache.spark.sql.jdbc.JdbcDialect
-
Check if this dialect instance can handle a certain jdbc url.
- canHandle(String) - 类 中的静态方法org.apache.spark.sql.jdbc.MsSqlServerDialect
-
- canHandle(String) - 类 中的静态方法org.apache.spark.sql.jdbc.MySQLDialect
-
- canHandle(String) - 类 中的静态方法org.apache.spark.sql.jdbc.NoopDialect
-
- canHandle(String) - 类 中的静态方法org.apache.spark.sql.jdbc.OracleDialect
-
- canHandle(String) - 类 中的静态方法org.apache.spark.sql.jdbc.PostgresDialect
-
- canHandle(String) - 类 中的静态方法org.apache.spark.sql.jdbc.TeradataDialect
-
- CanonicalRandomVertexCut$() - 类 的构造器org.apache.spark.graphx.PartitionStrategy.CanonicalRandomVertexCut$
-
- canWrite(DataType, DataType, boolean, Function2<String, String, Object>, String, Enumeration.Value, Function1<String, BoxedUnit>) - 类 中的静态方法org.apache.spark.sql.types.DataType
-
Returns true if the write data type can be read using the read data type.
- capabilities() - 接口 中的方法org.apache.spark.sql.connector.catalog.Table
-
Returns the set of capabilities for this table.
- cartesian(JavaRDDLike<U, ?>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of
elements (a, b) where a is in this and b is in other.
- cartesian(RDD<U>, ClassTag<U>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of
elements (a, b) where a is in this and b is in other.
- CaseInsensitiveStringMap - org.apache.spark.sql.util中的类
-
Case-insensitive map of string keys to string values.
- CaseInsensitiveStringMap(Map<String, String>) - 类 的构造器org.apache.spark.sql.util.CaseInsensitiveStringMap
-
- caseSensitive() - 类 中的方法org.apache.spark.ml.feature.StopWordsRemover
-
Whether to do a case sensitive comparison over the stop words.
- cast(DataType) - 类 中的方法org.apache.spark.sql.Column
-
Casts the column to a different data type.
- cast(String) - 类 中的方法org.apache.spark.sql.Column
-
Casts the column to a different data type, using the canonical string representation
of the type.
- Catalog - org.apache.spark.sql.catalog中的类
-
Catalog interface for Spark.
- Catalog() - 类 的构造器org.apache.spark.sql.catalog.Catalog
-
- catalog() - 类 中的方法org.apache.spark.sql.SparkSession
-
- CatalogAndIdentifier() - 接口 中的方法org.apache.spark.sql.connector.catalog.LookupCatalog
-
- CatalogAndIdentifierParts() - 接口 中的方法org.apache.spark.sql.connector.catalog.LookupCatalog
-
- CatalogAndIdentifierParts() - 类 的构造器org.apache.spark.sql.connector.catalog.LookupCatalog.CatalogAndIdentifierParts
-
- CatalogAndIdentifierParts$() - 类 的构造器org.apache.spark.sql.connector.catalog.LookupCatalog.CatalogAndIdentifierParts$
-
- CatalogAndNamespace() - 接口 中的方法org.apache.spark.sql.connector.catalog.LookupCatalog
-
- CatalogAndNamespace() - 类 的构造器org.apache.spark.sql.connector.catalog.LookupCatalog.CatalogAndNamespace
-
- CatalogAndNamespace$() - 类 的构造器org.apache.spark.sql.connector.catalog.LookupCatalog.CatalogAndNamespace$
-
- CatalogExtension - org.apache.spark.sql.connector.catalog中的接口
-
An API to extend the Spark built-in session catalog.
- CatalogHelper(CatalogPlugin) - 类 的构造器org.apache.spark.sql.connector.catalog.CatalogV2Implicits.CatalogHelper
-
- catalogManager() - 接口 中的方法org.apache.spark.sql.connector.catalog.LookupCatalog
-
- CatalogNotFoundException - org.apache.spark.sql.connector.catalog中的异常错误
-
- CatalogNotFoundException(String, Throwable) - 异常错误 的构造器org.apache.spark.sql.connector.catalog.CatalogNotFoundException
-
- CatalogNotFoundException(String) - 异常错误 的构造器org.apache.spark.sql.connector.catalog.CatalogNotFoundException
-
- CatalogObjectIdentifier() - 接口 中的方法org.apache.spark.sql.connector.catalog.LookupCatalog
-
- CatalogObjectIdentifier() - 类 的构造器org.apache.spark.sql.connector.catalog.LookupCatalog.CatalogObjectIdentifier
-
- CatalogObjectIdentifier$() - 类 的构造器org.apache.spark.sql.connector.catalog.LookupCatalog.CatalogObjectIdentifier$
-
- CatalogPlugin - org.apache.spark.sql.connector.catalog中的接口
-
A marker interface to provide a catalog implementation for Spark.
- Catalogs - org.apache.spark.sql.connector.catalog中的类
-
- catalogString() - 类 中的方法org.apache.spark.sql.types.ArrayType
-
- catalogString() - 类 中的静态方法org.apache.spark.sql.types.BinaryType
-
- catalogString() - 类 中的静态方法org.apache.spark.sql.types.BooleanType
-
- catalogString() - 类 中的静态方法org.apache.spark.sql.types.ByteType
-
- catalogString() - 类 中的静态方法org.apache.spark.sql.types.CalendarIntervalType
-
- catalogString() - 类 中的方法org.apache.spark.sql.types.DataType
-
String representation for the type saved in external catalogs.
- catalogString() - 类 中的静态方法org.apache.spark.sql.types.DateType
-
- catalogString() - 类 中的静态方法org.apache.spark.sql.types.DoubleType
-
- catalogString() - 类 中的静态方法org.apache.spark.sql.types.FloatType
-
- catalogString() - 类 中的静态方法org.apache.spark.sql.types.IntegerType
-
- catalogString() - 类 中的静态方法org.apache.spark.sql.types.LongType
-
- catalogString() - 类 中的方法org.apache.spark.sql.types.MapType
-
- catalogString() - 类 中的静态方法org.apache.spark.sql.types.NullType
-
- catalogString() - 类 中的静态方法org.apache.spark.sql.types.ShortType
-
- catalogString() - 类 中的静态方法org.apache.spark.sql.types.StringType
-
- catalogString() - 类 中的方法org.apache.spark.sql.types.StructType
-
- catalogString() - 类 中的静态方法org.apache.spark.sql.types.TimestampType
-
- CatalogV2Implicits - org.apache.spark.sql.connector.catalog中的类
-
- CatalogV2Implicits() - 类 的构造器org.apache.spark.sql.connector.catalog.CatalogV2Implicits
-
- CatalogV2Implicits.BucketSpecHelper - org.apache.spark.sql.connector.catalog中的类
-
- CatalogV2Implicits.CatalogHelper - org.apache.spark.sql.connector.catalog中的类
-
- CatalogV2Implicits.IdentifierHelper - org.apache.spark.sql.connector.catalog中的类
-
- CatalogV2Implicits.MultipartIdentifierHelper - org.apache.spark.sql.connector.catalog中的类
-
- CatalogV2Implicits.NamespaceHelper - org.apache.spark.sql.connector.catalog中的类
-
- CatalogV2Implicits.PartitionTypeHelper - org.apache.spark.sql.connector.catalog中的类
-
- CatalogV2Implicits.TransformHelper - org.apache.spark.sql.connector.catalog中的类
-
- CatalogV2Util - org.apache.spark.sql.connector.catalog中的类
-
- CatalogV2Util() - 类 的构造器org.apache.spark.sql.connector.catalog.CatalogV2Util
-
- CatalystScan - org.apache.spark.sql.sources中的接口
-
::Experimental::
An interface for experimenting with a more direct connection to the query planner.
- Categorical() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.FeatureType
-
- categoricalCols() - 类 中的方法org.apache.spark.ml.feature.FeatureHasher
-
Numeric columns to treat as categorical features.
- categoricalFeaturesInfo() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- CategoricalSplit - org.apache.spark.ml.tree中的类
-
Split which tests a categorical feature.
- categories() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.SplitData
-
- categories() - 类 中的方法org.apache.spark.mllib.tree.model.Split
-
- categoryMaps() - 类 中的方法org.apache.spark.ml.feature.VectorIndexerModel
-
- categorySizes() - 类 中的方法org.apache.spark.ml.feature.OneHotEncoderModel
-
- cause() - 异常错误 中的方法org.apache.spark.sql.AnalysisException
-
- cause() - 异常错误 中的方法org.apache.spark.sql.streaming.StreamingQueryException
-
- CausedBy - org.apache.spark.util中的类
-
Extractor Object for pulling out the root cause of an error.
- CausedBy() - 类 的构造器org.apache.spark.util.CausedBy
-
- cbrt(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the cube-root of the given value.
- cbrt(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the cube-root of the given column.
- ceil(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the ceiling of the given value.
- ceil(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the ceiling of the given column.
- ceil() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- censorCol() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- censorCol() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- censorCol() - 接口 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionParams
-
Param for censor column name.
- chainl1(Function0<Parsers.Parser<T>>, Function0<Parsers.Parser<Function2<T, T, T>>>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- chainl1(Function0<Parsers.Parser<T>>, Function0<Parsers.Parser<U>>, Function0<Parsers.Parser<Function2<T, U, T>>>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- chainr1(Function0<Parsers.Parser<T>>, Function0<Parsers.Parser<Function2<T, U, U>>>, Function2<T, U, U>, U) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- changePrecision(int, int) - 类 中的方法org.apache.spark.sql.types.Decimal
-
Update precision and scale while keeping our value the same, and return true if successful.
- channel() - 接口 中的方法org.apache.spark.shuffle.api.WritableByteChannelWrapper
-
The underlying channel to write bytes into.
- channelRead0(ChannelHandlerContext, byte[]) - 类 中的方法org.apache.spark.api.r.RBackendAuthHandler
-
- CharType - org.apache.spark.sql.types中的类
-
Hive char type.
- CharType(int) - 类 的构造器org.apache.spark.sql.types.CharType
-
- checkAndGetK8sMasterUrl(String) - 类 中的静态方法org.apache.spark.util.Utils
-
Check the validity of the given Kubernetes master URL and return the resolved URL.
- checkColumnNameDuplication(Seq<String>, String, Function2<String, String, Object>) - 类 中的静态方法org.apache.spark.sql.util.SchemaUtils
-
Checks if input column names have duplicate identifiers.
- checkColumnNameDuplication(Seq<String>, String, boolean) - 类 中的静态方法org.apache.spark.sql.util.SchemaUtils
-
Checks if input column names have duplicate identifiers.
- checkColumnType(StructType, String, DataType, String) - 类 中的静态方法org.apache.spark.ml.util.SchemaUtils
-
Check whether the given schema contains a column of the required data type.
- checkColumnTypes(StructType, String, Seq<DataType>, String) - 类 中的静态方法org.apache.spark.ml.util.SchemaUtils
-
Check whether the given schema contains a column of one of the require data types.
- checkDataColumns(RFormula, Dataset<?>) - 类 中的静态方法org.apache.spark.ml.r.RWrapperUtils
-
DataFrame column check.
- checkedCast() - 接口 中的方法org.apache.spark.ml.recommendation.ALSModelParams
-
Attempts to safely cast a user/item id to an Int.
- checkFileExists(String, Configuration) - 类 中的静态方法org.apache.spark.streaming.util.HdfsUtils
-
Check if the file exists at the given path.
- checkHost(String) - 类 中的静态方法org.apache.spark.util.Utils
-
- checkHostPort(String) - 类 中的静态方法org.apache.spark.util.Utils
-
- checkNumericType(StructType, String, String) - 类 中的静态方法org.apache.spark.ml.util.SchemaUtils
-
Check whether the given schema contains a column of the numeric data type.
- checkpoint() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Mark this RDD for checkpointing.
- checkpoint() - 类 中的方法org.apache.spark.graphx.Graph
-
Mark this Graph for checkpointing.
- checkpoint() - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
- checkpoint() - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- checkpoint() - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- checkpoint() - 类 中的方法org.apache.spark.rdd.HadoopRDD
-
- checkpoint() - 类 中的方法org.apache.spark.rdd.RDD
-
Mark this RDD for checkpointing.
- checkpoint() - 类 中的方法org.apache.spark.sql.Dataset
-
Eagerly checkpoint a Dataset and return the new Dataset.
- checkpoint(boolean) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a checkpointed version of this Dataset.
- checkpoint(Duration) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Enable periodic checkpointing of RDDs of this DStream.
- checkpoint(String) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Sets the context to periodically checkpoint the DStream operations for master
fault-tolerance.
- checkpoint(Duration) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Enable periodic checkpointing of RDDs of this DStream
- checkpoint(String) - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Set the context to periodically checkpoint the DStream operations for driver
fault-tolerance.
- checkpointCleaned(long) - 接口 中的方法org.apache.spark.CleanerListener
-
- Checkpointed() - 类 中的静态方法org.apache.spark.rdd.CheckpointState
-
- CheckpointingInProgress() - 类 中的静态方法org.apache.spark.rdd.CheckpointState
-
- checkpointInterval() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- checkpointInterval() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- checkpointInterval() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- checkpointInterval() - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- checkpointInterval() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- checkpointInterval() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- checkpointInterval() - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- checkpointInterval() - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
- checkpointInterval() - 接口 中的方法org.apache.spark.ml.param.shared.HasCheckpointInterval
-
Param for set checkpoint interval (>= 1) or disable checkpoint (-1).
- checkpointInterval() - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- checkpointInterval() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- checkpointInterval() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- checkpointInterval() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- checkpointInterval() - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- checkpointInterval() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- checkpointInterval() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- checkpointInterval() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- CheckpointReader - org.apache.spark.streaming中的类
-
- CheckpointReader() - 类 的构造器org.apache.spark.streaming.CheckpointReader
-
- CheckpointState - org.apache.spark.rdd中的类
-
Enumeration to manage state transitions of an RDD through checkpointing
[ Initialized --> checkpointing in progress --> checkpointed ]
- CheckpointState() - 类 的构造器org.apache.spark.rdd.CheckpointState
-
- checkSchemaColumnNameDuplication(StructType, String, boolean) - 类 中的静态方法org.apache.spark.sql.util.SchemaUtils
-
Checks if an input schema has duplicate column names.
- checkSingleVsMultiColumnParams(Params, Seq<Param<?>>, Seq<Param<?>>) - 类 中的静态方法org.apache.spark.ml.param.ParamValidators
-
Utility for Param validity checks for Transformers which have both single- and multi-column
support.
- checkSpeculatableTasks(int) - 接口 中的方法org.apache.spark.scheduler.Schedulable
-
- checkState(boolean, Function0<String>) - 类 中的静态方法org.apache.spark.streaming.util.HdfsUtils
-
- checkThresholdConsistency() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionParams
-
If threshold and thresholds are both set, ensures they are consistent.
- checkTransformDuplication(Seq<Transform>, String, boolean) - 类 中的静态方法org.apache.spark.sql.util.SchemaUtils
-
Checks if the partitioning transforms are being duplicated or not.
- child() - 类 中的方法org.apache.spark.sql.hive.execution.ScriptTransformationExec
-
- child() - 类 中的方法org.apache.spark.sql.sources.Not
-
- CHILD_CONNECTION_TIMEOUT - 类 中的静态变量org.apache.spark.launcher.SparkLauncher
-
Maximum time (in ms) to wait for a child process to connect back to the launcher server
when using @link{#start()}.
- CHILD_PROCESS_LOGGER_NAME - 类 中的静态变量org.apache.spark.launcher.SparkLauncher
-
Logger name to use when launching a child process.
- ChildFirstURLClassLoader - org.apache.spark.util中的类
-
A mutable class loader that gives preference to its own URLs over the parent class loader
when loading classes and resources.
- ChildFirstURLClassLoader(URL[], ClassLoader) - 类 的构造器org.apache.spark.util.ChildFirstURLClassLoader
-
- chiSqFunc() - 类 中的方法org.apache.spark.mllib.stat.test.ChiSqTest.Method
-
- ChiSqSelector - org.apache.spark.ml.feature中的类
-
Chi-Squared feature selection, which selects categorical features to use for predicting a
categorical label.
- ChiSqSelector(String) - 类 的构造器org.apache.spark.ml.feature.ChiSqSelector
-
- ChiSqSelector() - 类 的构造器org.apache.spark.ml.feature.ChiSqSelector
-
- ChiSqSelector - org.apache.spark.mllib.feature中的类
-
Creates a ChiSquared feature selector.
- ChiSqSelector() - 类 的构造器org.apache.spark.mllib.feature.ChiSqSelector
-
- ChiSqSelector(int) - 类 的构造器org.apache.spark.mllib.feature.ChiSqSelector
-
The is the same to call this() and setNumTopFeatures(numTopFeatures)
- ChiSqSelectorModel - org.apache.spark.ml.feature中的类
-
- ChiSqSelectorModel - org.apache.spark.mllib.feature中的类
-
Chi Squared selector model.
- ChiSqSelectorModel(int[]) - 类 的构造器org.apache.spark.mllib.feature.ChiSqSelectorModel
-
- ChiSqSelectorModel.SaveLoadV1_0$ - org.apache.spark.mllib.feature中的类
-
- ChiSqSelectorModel.SaveLoadV1_0$.Data - org.apache.spark.mllib.feature中的类
-
Model data for import/export
- ChiSqSelectorModel.SaveLoadV1_0$.Data$ - org.apache.spark.mllib.feature中的类
-
- ChiSqSelectorParams - org.apache.spark.ml.feature中的接口
-
- chiSqTest(Vector, Vector) - 类 中的静态方法org.apache.spark.mllib.stat.Statistics
-
Conduct Pearson's chi-squared goodness of fit test of the observed data against the
expected distribution.
- chiSqTest(Vector) - 类 中的静态方法org.apache.spark.mllib.stat.Statistics
-
Conduct Pearson's chi-squared goodness of fit test of the observed data against the uniform
distribution, with each category having an expected frequency of 1 / observed.size.
- chiSqTest(Matrix) - 类 中的静态方法org.apache.spark.mllib.stat.Statistics
-
Conduct Pearson's independence test on the input contingency matrix, which cannot contain
negative entries or columns or rows that sum up to 0.
- chiSqTest(RDD<LabeledPoint>) - 类 中的静态方法org.apache.spark.mllib.stat.Statistics
-
Conduct Pearson's independence test for every feature against the label across the input RDD.
- chiSqTest(JavaRDD<LabeledPoint>) - 类 中的静态方法org.apache.spark.mllib.stat.Statistics
-
Java-friendly version of chiSqTest()
- ChiSqTest - org.apache.spark.mllib.stat.test中的类
-
Conduct the chi-squared test for the input RDDs using the specified method.
- ChiSqTest() - 类 的构造器org.apache.spark.mllib.stat.test.ChiSqTest
-
- ChiSqTest.Method - org.apache.spark.mllib.stat.test中的类
-
param: name String name for the method.
- ChiSqTest.Method$ - org.apache.spark.mllib.stat.test中的类
-
- ChiSqTest.NullHypothesis$ - org.apache.spark.mllib.stat.test中的类
-
- ChiSqTestResult - org.apache.spark.mllib.stat.test中的类
-
Object containing the test results for the chi-squared hypothesis test.
- chiSquared(Vector, Vector, String) - 类 中的静态方法org.apache.spark.mllib.stat.test.ChiSqTest
-
- chiSquaredFeatures(RDD<LabeledPoint>, String) - 类 中的静态方法org.apache.spark.mllib.stat.test.ChiSqTest
-
Conduct Pearson's independence test for each feature against the label across the input RDD.
- chiSquaredMatrix(Matrix, String) - 类 中的静态方法org.apache.spark.mllib.stat.test.ChiSqTest
-
- ChiSquareTest - org.apache.spark.ml.stat中的类
-
Chi-square hypothesis testing for categorical data.
- ChiSquareTest() - 类 的构造器org.apache.spark.ml.stat.ChiSquareTest
-
- chmod700(File) - 类 中的静态方法org.apache.spark.util.Utils
-
JDK equivalent of chmod 700 file.
- CholeskyDecomposition - org.apache.spark.mllib.linalg中的类
-
Compute Cholesky decomposition.
- CholeskyDecomposition() - 类 的构造器org.apache.spark.mllib.linalg.CholeskyDecomposition
-
- cipherStream() - 接口 中的方法org.apache.spark.security.CryptoStreamUtils.BaseErrorHandler
-
The encrypted stream that may get into an unhealthy state.
- classForName(String, boolean, boolean) - 类 中的静态方法org.apache.spark.util.Utils
-
Preferred alternative to Class.forName(className), as well as
Class.forName(className, initialize, loader) with current thread's ContextClassLoader.
- Classification() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.Algo
-
- ClassificationLoss - org.apache.spark.mllib.tree.loss中的接口
-
- ClassificationModel<FeaturesType,M extends ClassificationModel<FeaturesType,M>> - org.apache.spark.ml.classification中的类
-
:: DeveloperApi ::
Model produced by a
Classifier.
- ClassificationModel() - 类 的构造器org.apache.spark.ml.classification.ClassificationModel
-
- ClassificationModel - org.apache.spark.mllib.classification中的接口
-
Represents a classification model that predicts to which of a set of categories an example
belongs.
- Classifier<FeaturesType,E extends Classifier<FeaturesType,E,M>,M extends ClassificationModel<FeaturesType,M>> - org.apache.spark.ml.classification中的类
-
:: DeveloperApi ::
Single-label binary or multiclass classification.
- Classifier() - 类 的构造器org.apache.spark.ml.classification.Classifier
-
- classifier() - 类 中的方法org.apache.spark.ml.classification.OneVsRest
-
- classifier() - 类 中的方法org.apache.spark.ml.classification.OneVsRestModel
-
- classifier() - 接口 中的方法org.apache.spark.ml.classification.OneVsRestParams
-
param for the base binary classifier that we reduce multiclass classification into.
- ClassifierParams - org.apache.spark.ml.classification中的接口
-
(private[spark]) Params for classification.
- ClassifierTypeTrait - org.apache.spark.ml.classification中的接口
-
- classIsLoadable(String) - 类 中的静态方法org.apache.spark.util.Utils
-
Determines whether the provided class is loadable in the current thread.
- className() - 类 中的方法org.apache.spark.ExceptionFailure
-
- className() - 类 中的静态方法org.apache.spark.ml.linalg.JsonMatrixConverter
-
Unique class name for identifying JSON object encoded by this class.
- className() - 类 中的方法org.apache.spark.sql.catalog.Function
-
- classpathEntries() - 类 中的方法org.apache.spark.status.api.v1.ApplicationEnvironmentInfo
-
- classTag() - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
- classTag() - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
- classTag() - 类 中的方法org.apache.spark.api.java.JavaRDD
-
- classTag() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
- classTag() - 类 中的方法org.apache.spark.sql.Dataset
-
- classTag() - 类 中的方法org.apache.spark.storage.memory.DeserializedMemoryEntry
-
- classTag() - 接口 中的方法org.apache.spark.storage.memory.MemoryEntry
-
- classTag() - 类 中的方法org.apache.spark.storage.memory.SerializedMemoryEntry
-
- classTag() - 类 中的方法org.apache.spark.streaming.api.java.JavaDStream
-
- classTag() - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
- classTag() - 类 中的方法org.apache.spark.streaming.api.java.JavaInputDStream
-
- classTag() - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
- classTag() - 类 中的方法org.apache.spark.streaming.api.java.JavaReceiverInputDStream
-
- clean(long, boolean) - 类 中的方法org.apache.spark.streaming.util.WriteAheadLog
-
Clean all the records that are older than the threshold time.
- clean(Object, boolean, boolean) - 类 中的静态方法org.apache.spark.util.ClosureCleaner
-
Clean the given closure in place.
- CleanAccum - org.apache.spark中的类
-
- CleanAccum(long) - 类 的构造器org.apache.spark.CleanAccum
-
- CleanBroadcast - org.apache.spark中的类
-
- CleanBroadcast(long) - 类 的构造器org.apache.spark.CleanBroadcast
-
- CleanCheckpoint - org.apache.spark中的类
-
- CleanCheckpoint(int) - 类 的构造器org.apache.spark.CleanCheckpoint
-
- CLEANER_ENABLED() - 类 中的静态方法org.apache.spark.internal.config.History
-
- CLEANER_INTERVAL_S() - 类 中的静态方法org.apache.spark.internal.config.History
-
- CleanerListener - org.apache.spark中的接口
-
Listener class used for testing when any item has been cleaned by the Cleaner class.
- cleaning() - 类 中的方法org.apache.spark.status.LiveStage
-
- CleanRDD - org.apache.spark中的类
-
- CleanRDD(int) - 类 的构造器org.apache.spark.CleanRDD
-
- CleanShuffle - org.apache.spark中的类
-
- CleanShuffle(int) - 类 的构造器org.apache.spark.CleanShuffle
-
- cleanupApplication() - 接口 中的方法org.apache.spark.shuffle.api.ShuffleDriverComponents
-
Called once at the end of the Spark application to clean up any existing shuffle state.
- CleanupDynamicPruningFilters - org.apache.spark.sql.dynamicpruning中的类
-
Removes the filter nodes with dynamic pruning that were not pushed down to the scan.
- CleanupDynamicPruningFilters() - 类 的构造器org.apache.spark.sql.dynamicpruning.CleanupDynamicPruningFilters
-
- cleanupOldBlocks(long) - 接口 中的方法org.apache.spark.streaming.receiver.ReceivedBlockHandler
-
Cleanup old blocks older than the given threshold time
- CleanupTask - org.apache.spark中的接口
-
Classes that represent cleaning tasks.
- CleanupTaskWeakReference - org.apache.spark中的类
-
A WeakReference associated with a CleanupTask.
- CleanupTaskWeakReference(CleanupTask, Object, ReferenceQueue<Object>) - 类 的构造器org.apache.spark.CleanupTaskWeakReference
-
- clear(Param<?>) - 接口 中的方法org.apache.spark.ml.param.Params
-
Clears the user-supplied value for the input param.
- clear() - 类 中的方法org.apache.spark.sql.util.CaseInsensitiveStringMap
-
- clear() - 类 中的方法org.apache.spark.sql.util.ExecutionListenerManager
-
- clear() - 类 中的静态方法org.apache.spark.util.AccumulatorContext
-
- clearActiveSession() - 类 中的静态方法org.apache.spark.sql.SparkSession
-
Clears the active SparkSession for current thread.
- clearCache() - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Removes all cached tables from the in-memory cache.
- clearCache() - 类 中的方法org.apache.spark.sql.SQLContext
-
Removes all cached tables from the in-memory cache.
- clearCallSite() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Pass-through to SparkContext.setCallSite.
- clearCallSite() - 类 中的方法org.apache.spark.SparkContext
-
Clear the thread-local property for overriding the call sites
of actions and RDDs.
- clearDefaultSession() - 类 中的静态方法org.apache.spark.sql.SparkSession
-
Clears the default SparkSession that is returned by the builder.
- clearDependencies() - 类 中的方法org.apache.spark.rdd.CoGroupedRDD
-
- clearDependencies() - 类 中的方法org.apache.spark.rdd.ShuffledRDD
-
- clearDependencies() - 类 中的方法org.apache.spark.rdd.UnionRDD
-
- clearJobGroup() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Clear the current thread's job group ID and its description.
- clearJobGroup() - 类 中的方法org.apache.spark.SparkContext
-
Clear the current thread's job group ID and its description.
- clearThreshold() - 类 中的方法org.apache.spark.mllib.classification.LogisticRegressionModel
-
Clears the threshold so that predict will output raw prediction scores.
- clearThreshold() - 类 中的方法org.apache.spark.mllib.classification.SVMModel
-
Clears the threshold so that predict will output raw prediction scores.
- Clock - org.apache.spark.util中的接口
-
An interface to represent clocks, so that they can be mocked out in unit tests.
- CLogLog$() - 类 的构造器org.apache.spark.ml.regression.GeneralizedLinearRegression.CLogLog$
-
- clone() - 类 中的方法org.apache.spark.SparkConf
-
Copy this object
- clone() - 类 中的方法org.apache.spark.sql.ExperimentalMethods
-
- clone() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- clone() - 类 中的方法org.apache.spark.storage.StorageLevel
-
- clone() - 类 中的方法org.apache.spark.util.random.BernoulliCellSampler
-
- clone() - 类 中的方法org.apache.spark.util.random.BernoulliSampler
-
- clone() - 类 中的方法org.apache.spark.util.random.PoissonSampler
-
- clone() - 接口 中的方法org.apache.spark.util.random.RandomSampler
-
return a copy of the RandomSampler object
- clone(T, SerializerInstance, ClassTag<T>) - 类 中的静态方法org.apache.spark.util.Utils
-
Clone an object using a Spark serializer.
- cloneComplement() - 类 中的方法org.apache.spark.util.random.BernoulliCellSampler
-
Return a sampler that is the complement of the range specified of the current sampler.
- cloneProperties(Properties) - 类 中的静态方法org.apache.spark.util.Utils
-
Create a new properties object with the same values as `props`
- close() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
- close() - 类 中的方法org.apache.spark.io.NioBufferedFileInputStream
-
- close() - 类 中的方法org.apache.spark.io.ReadAheadInputStream
-
- close() - 接口 中的方法org.apache.spark.security.CryptoStreamUtils.BaseErrorHandler
-
- close() - 类 中的方法org.apache.spark.serializer.DeserializationStream
-
- close() - 类 中的方法org.apache.spark.serializer.SerializationStream
-
- close(Throwable) - 类 中的方法org.apache.spark.sql.ForeachWriter
-
Called when stopping to process one partition of new data in the executor side.
- close() - 类 中的方法org.apache.spark.sql.hive.execution.HiveOutputWriter
-
- close() - 类 中的方法org.apache.spark.sql.SparkSession
-
Synonym for stop().
- close() - 类 中的方法org.apache.spark.sql.vectorized.ArrowColumnVector
-
- close() - 类 中的方法org.apache.spark.sql.vectorized.ColumnarBatch
-
Called to close all the columns in this batch.
- close() - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Cleans up memory for this column vector.
- close() - 类 中的方法org.apache.spark.storage.BufferReleasingInputStream
-
- close() - 类 中的方法org.apache.spark.storage.CountingWritableChannel
-
- close() - 类 中的方法org.apache.spark.storage.TimeTrackingOutputStream
-
- close() - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
- close() - 类 中的方法org.apache.spark.streaming.util.WriteAheadLog
-
Close this log and release any resources.
- closeWriter(TaskAttemptContext) - 类 中的方法org.apache.spark.internal.io.HadoopWriteConfigUtil
-
- ClosureCleaner - org.apache.spark.util中的类
-
A cleaner that renders closures serializable if they can be done so safely.
- ClosureCleaner() - 类 的构造器org.apache.spark.util.ClosureCleaner
-
- closureSerializer() - 类 中的方法org.apache.spark.SparkEnv
-
- cls() - 类 中的方法org.apache.spark.sql.types.ObjectType
-
- cls() - 类 中的方法org.apache.spark.util.MethodIdentifier
-
- clsTag() - 接口 中的方法org.apache.spark.sql.Encoder
-
A ClassTag that can be used to construct an Array to contain a collection of T.
- cluster() - 类 中的方法org.apache.spark.ml.clustering.ClusteringSummary
-
- cluster() - 类 中的方法org.apache.spark.mllib.clustering.PowerIterationClustering.Assignment
-
- clusterCenter() - 类 中的方法org.apache.spark.ml.clustering.ClusterData
-
- clusterCenters() - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
- clusterCenters() - 类 中的方法org.apache.spark.ml.clustering.KMeansModel
-
- clusterCenters() - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeansModel
-
Leaf cluster centers.
- clusterCenters() - 类 中的方法org.apache.spark.mllib.clustering.KMeansModel
-
- clusterCenters() - 类 中的方法org.apache.spark.mllib.clustering.StreamingKMeansModel
-
- ClusterData - org.apache.spark.ml.clustering中的类
-
Helper class for storing model data
- ClusterData(int, Vector) - 类 的构造器org.apache.spark.ml.clustering.ClusterData
-
- clusteredColumns - 类 中的变量org.apache.spark.sql.connector.read.partitioning.ClusteredDistribution
-
The names of the clustered columns.
- ClusteredDistribution - org.apache.spark.sql.connector.read.partitioning中的类
-
- ClusteredDistribution(String[]) - 类 的构造器org.apache.spark.sql.connector.read.partitioning.ClusteredDistribution
-
- clusterIdx() - 类 中的方法org.apache.spark.ml.clustering.ClusterData
-
- ClusteringEvaluator - org.apache.spark.ml.evaluation中的类
-
Evaluator for clustering results.
- ClusteringEvaluator(String) - 类 的构造器org.apache.spark.ml.evaluation.ClusteringEvaluator
-
- ClusteringEvaluator() - 类 的构造器org.apache.spark.ml.evaluation.ClusteringEvaluator
-
- ClusteringSummary - org.apache.spark.ml.clustering中的类
-
Summary of clustering algorithms.
- CLUSTERS_CONFIG_PREFIX() - 类 中的静态方法org.apache.spark.kafka010.KafkaTokenSparkConf
-
- clusterSizes() - 类 中的方法org.apache.spark.ml.clustering.ClusteringSummary
-
- ClusterStats(Vector, double, long) - 类 的构造器org.apache.spark.ml.evaluation.SquaredEuclideanSilhouette.ClusterStats
-
- ClusterStats$() - 类 的构造器org.apache.spark.ml.evaluation.SquaredEuclideanSilhouette.ClusterStats$
-
- clusterWeights() - 类 中的方法org.apache.spark.mllib.clustering.StreamingKMeansModel
-
- cn() - 类 中的方法org.apache.spark.mllib.feature.VocabWord
-
- coalesce(int) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Return a new RDD that is reduced into numPartitions partitions.
- coalesce(int, boolean) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Return a new RDD that is reduced into numPartitions partitions.
- coalesce(int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return a new RDD that is reduced into numPartitions partitions.
- coalesce(int, boolean) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return a new RDD that is reduced into numPartitions partitions.
- coalesce(int) - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Return a new RDD that is reduced into numPartitions partitions.
- coalesce(int, boolean) - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Return a new RDD that is reduced into numPartitions partitions.
- coalesce(int, RDD<?>) - 类 中的方法org.apache.spark.rdd.DefaultPartitionCoalescer
-
Runs the packing algorithm and returns an array of PartitionGroups that if possible are
load balanced and grouped by locality
- coalesce(int, RDD<?>) - 接口 中的方法org.apache.spark.rdd.PartitionCoalescer
-
Coalesce the partitions of the given RDD.
- coalesce(int, boolean, Option<PartitionCoalescer>, Ordering<T>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return a new RDD that is reduced into numPartitions partitions.
- coalesce(int) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset that has exactly numPartitions partitions, when the fewer partitions
are requested.
- coalesce(Column...) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the first column that is not null, or null if all inputs are null.
- coalesce(Seq<Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the first column that is not null, or null if all inputs are null.
- CoarseGrainedClusterMessage - org.apache.spark.scheduler.cluster中的接口
-
- CoarseGrainedClusterMessages - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages
-
- CoarseGrainedClusterMessages.AddWebUIFilter - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.AddWebUIFilter$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.GetExecutorLossReason - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.GetExecutorLossReason$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.KillExecutors - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.KillExecutors$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.KillExecutorsOnHost - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.KillExecutorsOnHost$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.KillTask - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.KillTask$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.LaunchTask - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.LaunchTask$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.RegisterClusterManager - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.RegisterClusterManager$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.RegisteredExecutor$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.RegisterExecutor - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.RegisterExecutor$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.RegisterExecutorFailed - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.RegisterExecutorFailed$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.RegisterExecutorResponse - org.apache.spark.scheduler.cluster中的接口
-
- CoarseGrainedClusterMessages.RemoveExecutor - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.RemoveExecutor$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.RemoveWorker - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.RemoveWorker$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.RequestExecutors - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.RequestExecutors$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.RetrieveDelegationTokens$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.RetrieveLastAllocatedExecutorId$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.RetrieveSparkAppConfig$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.ReviveOffers$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.SetupDriver - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.SetupDriver$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.Shutdown$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.SparkAppConfig - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.SparkAppConfig$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.StatusUpdate - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.StatusUpdate$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.StopDriver$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.StopExecutor$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.StopExecutors$ - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.UpdateDelegationTokens - org.apache.spark.scheduler.cluster中的类
-
- CoarseGrainedClusterMessages.UpdateDelegationTokens$ - org.apache.spark.scheduler.cluster中的类
-
- code() - 类 中的方法org.apache.spark.mllib.feature.VocabWord
-
- CodegenMetrics - org.apache.spark.metrics.source中的类
-
Metrics for code generation.
- CodegenMetrics() - 类 的构造器org.apache.spark.metrics.source.CodegenMetrics
-
- codeLen() - 类 中的方法org.apache.spark.mllib.feature.VocabWord
-
- coefficientMatrix() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- coefficients() - 类 中的方法org.apache.spark.ml.classification.LinearSVCModel
-
- coefficients() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
A vector of model coefficients for "binomial" logistic regression.
- coefficients() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- coefficients() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
- coefficients() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionModel
-
- coefficientStandardErrors() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionTrainingSummary
-
- coefficientStandardErrors() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionSummary
-
- cogroup(JavaPairRDD<K, W>, Partitioner) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
For each key k in this or other, return a resulting RDD that contains a tuple with the
list of values for that key in this as well as other.
- cogroup(JavaPairRDD<K, W1>, JavaPairRDD<K, W2>, Partitioner) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
For each key k in this or other1 or other2, return a resulting RDD that contains a
tuple with the list of values for that key in this, other1 and other2.
- cogroup(JavaPairRDD<K, W1>, JavaPairRDD<K, W2>, JavaPairRDD<K, W3>, Partitioner) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
For each key k in this or other1 or other2 or other3,
return a resulting RDD that contains a tuple with the list of values
for that key in this, other1, other2 and other3.
- cogroup(JavaPairRDD<K, W>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
For each key k in this or other, return a resulting RDD that contains a tuple with the
list of values for that key in this as well as other.
- cogroup(JavaPairRDD<K, W1>, JavaPairRDD<K, W2>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
For each key k in this or other1 or other2, return a resulting RDD that contains a
tuple with the list of values for that key in this, other1 and other2.
- cogroup(JavaPairRDD<K, W1>, JavaPairRDD<K, W2>, JavaPairRDD<K, W3>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
For each key k in this or other1 or other2 or other3,
return a resulting RDD that contains a tuple with the list of values
for that key in this, other1, other2 and other3.
- cogroup(JavaPairRDD<K, W>, int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
For each key k in this or other, return a resulting RDD that contains a tuple with the
list of values for that key in this as well as other.
- cogroup(JavaPairRDD<K, W1>, JavaPairRDD<K, W2>, int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
For each key k in this or other1 or other2, return a resulting RDD that contains a
tuple with the list of values for that key in this, other1 and other2.
- cogroup(JavaPairRDD<K, W1>, JavaPairRDD<K, W2>, JavaPairRDD<K, W3>, int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
For each key k in this or other1 or other2 or other3,
return a resulting RDD that contains a tuple with the list of values
for that key in this, other1, other2 and other3.
- cogroup(RDD<Tuple2<K, W1>>, RDD<Tuple2<K, W2>>, RDD<Tuple2<K, W3>>, Partitioner) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
For each key k in this or other1 or other2 or other3,
return a resulting RDD that contains a tuple with the list of values
for that key in this, other1, other2 and other3.
- cogroup(RDD<Tuple2<K, W>>, Partitioner) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
For each key k in this or other, return a resulting RDD that contains a tuple with the
list of values for that key in this as well as other.
- cogroup(RDD<Tuple2<K, W1>>, RDD<Tuple2<K, W2>>, Partitioner) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
For each key k in this or other1 or other2, return a resulting RDD that contains a
tuple with the list of values for that key in this, other1 and other2.
- cogroup(RDD<Tuple2<K, W1>>, RDD<Tuple2<K, W2>>, RDD<Tuple2<K, W3>>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
For each key k in this or other1 or other2 or other3,
return a resulting RDD that contains a tuple with the list of values
for that key in this, other1, other2 and other3.
- cogroup(RDD<Tuple2<K, W>>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
For each key k in this or other, return a resulting RDD that contains a tuple with the
list of values for that key in this as well as other.
- cogroup(RDD<Tuple2<K, W1>>, RDD<Tuple2<K, W2>>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
For each key k in this or other1 or other2, return a resulting RDD that contains a
tuple with the list of values for that key in this, other1 and other2.
- cogroup(RDD<Tuple2<K, W>>, int) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
For each key k in this or other, return a resulting RDD that contains a tuple with the
list of values for that key in this as well as other.
- cogroup(RDD<Tuple2<K, W1>>, RDD<Tuple2<K, W2>>, int) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
For each key k in this or other1 or other2, return a resulting RDD that contains a
tuple with the list of values for that key in this, other1 and other2.
- cogroup(RDD<Tuple2<K, W1>>, RDD<Tuple2<K, W2>>, RDD<Tuple2<K, W3>>, int) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
For each key k in this or other1 or other2 or other3,
return a resulting RDD that contains a tuple with the list of values
for that key in this, other1, other2 and other3.
- cogroup(KeyValueGroupedDataset<K, U>, Function3<K, Iterator<V>, Iterator<U>, TraversableOnce<R>>, Encoder<R>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
(Scala-specific)
Applies the given function to each cogrouped data.
- cogroup(KeyValueGroupedDataset<K, U>, CoGroupFunction<K, V, U, R>, Encoder<R>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
(Java-specific)
Applies the given function to each cogrouped data.
- cogroup(JavaPairDStream<K, W>) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.
- cogroup(JavaPairDStream<K, W>, int) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.
- cogroup(JavaPairDStream<K, W>, Partitioner) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.
- cogroup(DStream<Tuple2<K, W>>, ClassTag<W>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.
- cogroup(DStream<Tuple2<K, W>>, int, ClassTag<W>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.
- cogroup(DStream<Tuple2<K, W>>, Partitioner, ClassTag<W>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying 'cogroup' between RDDs of this DStream and other DStream.
- CoGroupedRDD<K> - org.apache.spark.rdd中的类
-
:: DeveloperApi ::
An RDD that cogroups its parents.
- CoGroupedRDD(Seq<RDD<? extends Product2<K, ?>>>, Partitioner, ClassTag<K>) - 类 的构造器org.apache.spark.rdd.CoGroupedRDD
-
- CoGroupFunction<K,V1,V2,R> - org.apache.spark.api.java.function中的接口
-
A function that returns zero or more output records from each grouping key and its values from 2
Datasets.
- col(String) - 类 中的方法org.apache.spark.sql.Dataset
-
Selects column based on the column name and returns it as a
Column.
- col(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns a
Column based on the given column name.
- COL_POS_KEY() - 类 中的静态方法org.apache.spark.sql.Dataset
-
- coldStartStrategy() - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- coldStartStrategy() - 类 中的方法org.apache.spark.ml.recommendation.ALSModel
-
- coldStartStrategy() - 接口 中的方法org.apache.spark.ml.recommendation.ALSModelParams
-
Param for strategy for dealing with unknown or new users/items at prediction time.
- colIter() - 类 中的方法org.apache.spark.ml.linalg.DenseMatrix
-
- colIter() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Returns an iterator of column vectors.
- colIter() - 类 中的方法org.apache.spark.ml.linalg.SparseMatrix
-
- colIter() - 类 中的方法org.apache.spark.mllib.linalg.DenseMatrix
-
- colIter() - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
Returns an iterator of column vectors.
- colIter() - 类 中的方法org.apache.spark.mllib.linalg.SparseMatrix
-
- collect() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return an array that contains all of the elements in this RDD.
- collect() - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
- collect() - 类 中的方法org.apache.spark.rdd.RDD
-
Return an array that contains all of the elements in this RDD.
- collect(PartialFunction<T, U>, ClassTag<U>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return an RDD that contains all matching values by applying f.
- collect() - 类 中的方法org.apache.spark.sql.Dataset
-
Returns an array that contains all rows in this Dataset.
- collect_list(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns a list of objects with duplicates.
- collect_list(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns a list of objects with duplicates.
- collect_set(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns a set of objects with duplicate elements eliminated.
- collect_set(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns a set of objects with duplicate elements eliminated.
- collectAsList() - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a Java list that contains all rows in this Dataset.
- collectAsMap() - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return the key-value pairs in this RDD to the master as a Map.
- collectAsMap() - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Return the key-value pairs in this RDD to the master as a Map.
- collectAsync() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
The asynchronous version of collect, which returns a future for
retrieving an array containing all of the elements in this RDD.
- collectAsync() - 类 中的方法org.apache.spark.rdd.AsyncRDDActions
-
Returns a future for retrieving all elements of this RDD.
- collectEdges(EdgeDirection) - 类 中的方法org.apache.spark.graphx.GraphOps
-
Returns an RDD that contains for each vertex v its local edges,
i.e., the edges that are incident on v, in the user-specified direction.
- collectionAccumulator() - 类 中的方法org.apache.spark.SparkContext
-
Create and register a CollectionAccumulator, which starts with empty list and accumulates
inputs by adding them into the list.
- collectionAccumulator(String) - 类 中的方法org.apache.spark.SparkContext
-
Create and register a CollectionAccumulator, which starts with empty list and accumulates
inputs by adding them into the list.
- CollectionAccumulator<T> - org.apache.spark.util中的类
-
- CollectionAccumulator() - 类 的构造器org.apache.spark.util.CollectionAccumulator
-
- CollectionsUtils - org.apache.spark.util中的类
-
- CollectionsUtils() - 类 的构造器org.apache.spark.util.CollectionsUtils
-
- collectNeighborIds(EdgeDirection) - 类 中的方法org.apache.spark.graphx.GraphOps
-
Collect the neighbor vertex ids for each vertex.
- collectNeighbors(EdgeDirection) - 类 中的方法org.apache.spark.graphx.GraphOps
-
Collect the neighbor vertex attributes for each vertex.
- collectPartitions(int[]) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return an array that contains all of the elements in a specific partition of this RDD.
- collectSubModels() - 接口 中的方法org.apache.spark.ml.param.shared.HasCollectSubModels
-
Param for whether to collect a list of sub-models trained during tuning.
- collectSubModels() - 类 中的方法org.apache.spark.ml.tuning.CrossValidator
-
- collectSubModels() - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplit
-
- colPtrs() - 类 中的方法org.apache.spark.ml.linalg.SparseMatrix
-
- colPtrs() - 类 中的方法org.apache.spark.mllib.linalg.SparseMatrix
-
- colRegex(String) - 类 中的方法org.apache.spark.sql.Dataset
-
Selects column based on the column name specified as a regex and returns it as
Column.
- colsPerBlock() - 类 中的方法org.apache.spark.mllib.linalg.distributed.BlockMatrix
-
- colStats(RDD<Vector>) - 类 中的静态方法org.apache.spark.mllib.stat.Statistics
-
Computes column-wise summary statistics for the input RDD[Vector].
- Column - org.apache.spark.sql.catalog中的类
-
A column in Spark, as returned by
listColumns method in
Catalog.
- Column(String, String, String, boolean, boolean, boolean) - 类 的构造器org.apache.spark.sql.catalog.Column
-
- Column - org.apache.spark.sql中的类
-
A column that will be computed based on the data in a DataFrame.
- Column(Expression) - 类 的构造器org.apache.spark.sql.Column
-
- Column(String) - 类 的构造器org.apache.spark.sql.Column
-
- column(String) - 类 中的静态方法org.apache.spark.sql.connector.expressions.Expressions
-
Create a named reference expression for a column.
- column(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns a
Column based on the given column name.
- column(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarBatch
-
Returns the column at `ordinal`.
- ColumnarArray - org.apache.spark.sql.vectorized中的类
-
- ColumnarArray(ColumnVector, int, int) - 类 的构造器org.apache.spark.sql.vectorized.ColumnarArray
-
- ColumnarBatch - org.apache.spark.sql.vectorized中的类
-
This class wraps multiple ColumnVectors as a row-wise table.
- ColumnarBatch(ColumnVector[]) - 类 的构造器org.apache.spark.sql.vectorized.ColumnarBatch
-
- ColumnarBatch(ColumnVector[], int) - 类 的构造器org.apache.spark.sql.vectorized.ColumnarBatch
-
Create a new batch from existing column vectors.
- ColumnarMap - org.apache.spark.sql.vectorized中的类
-
- ColumnarMap(ColumnVector, ColumnVector, int, int) - 类 的构造器org.apache.spark.sql.vectorized.ColumnarMap
-
- ColumnarRow - org.apache.spark.sql.vectorized中的类
-
- ColumnarRow(ColumnVector, int) - 类 的构造器org.apache.spark.sql.vectorized.ColumnarRow
-
- ColumnName - org.apache.spark.sql中的类
-
A convenient class used for constructing schema.
- ColumnName(String) - 类 的构造器org.apache.spark.sql.ColumnName
-
- ColumnPruner - org.apache.spark.ml.feature中的类
-
Utility transformer for removing temporary columns from a DataFrame.
- ColumnPruner(String, Set<String>) - 类 的构造器org.apache.spark.ml.feature.ColumnPruner
-
- ColumnPruner(Set<String>) - 类 的构造器org.apache.spark.ml.feature.ColumnPruner
-
- columns() - 类 中的方法org.apache.spark.sql.Dataset
-
Returns all column names as an array.
- columnSchema() - 类 中的静态方法org.apache.spark.ml.image.ImageSchema
-
Schema for the image column: Row(String, Int, Int, Int, Int, Array[Byte])
- columnSimilarities() - 类 中的方法org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
-
Compute all cosine similarities between columns of this matrix using the brute-force
approach of computing normalized dot products.
- columnSimilarities() - 类 中的方法org.apache.spark.mllib.linalg.distributed.RowMatrix
-
Compute all cosine similarities between columns of this matrix using the brute-force
approach of computing normalized dot products.
- columnSimilarities(double) - 类 中的方法org.apache.spark.mllib.linalg.distributed.RowMatrix
-
Compute similarities between columns of this matrix using a sampling approach.
- columnsToPrune() - 类 中的方法org.apache.spark.ml.feature.ColumnPruner
-
- columnToOldVector(Dataset<?>, String) - 类 中的静态方法org.apache.spark.ml.util.DatasetUtils
-
- columnToVector(Dataset<?>, String) - 类 中的静态方法org.apache.spark.ml.util.DatasetUtils
-
Cast a column in a Dataset to Vector type.
- ColumnVector - org.apache.spark.sql.vectorized中的类
-
An interface representing in-memory columnar data in Spark.
- combineByKey(Function<V, C>, Function2<C, V, C>, Function2<C, C, C>, Partitioner, boolean, Serializer) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Generic function to combine the elements for each key using a custom set of aggregation
functions.
- combineByKey(Function<V, C>, Function2<C, V, C>, Function2<C, C, C>, Partitioner) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Generic function to combine the elements for each key using a custom set of aggregation
functions.
- combineByKey(Function<V, C>, Function2<C, V, C>, Function2<C, C, C>, int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Simplified version of combineByKey that hash-partitions the output RDD and uses map-side
aggregation.
- combineByKey(Function<V, C>, Function2<C, V, C>, Function2<C, C, C>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Simplified version of combineByKey that hash-partitions the resulting RDD using the existing
partitioner/parallelism level and using map-side aggregation.
- combineByKey(Function1<V, C>, Function2<C, V, C>, Function2<C, C, C>, Partitioner, boolean, Serializer) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Generic function to combine the elements for each key using a custom set of aggregation
functions.
- combineByKey(Function1<V, C>, Function2<C, V, C>, Function2<C, C, C>, int) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Simplified version of combineByKeyWithClassTag that hash-partitions the output RDD.
- combineByKey(Function1<V, C>, Function2<C, V, C>, Function2<C, C, C>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Simplified version of combineByKeyWithClassTag that hash-partitions the resulting RDD using the
existing partitioner/parallelism level.
- combineByKey(Function<V, C>, Function2<C, V, C>, Function2<C, C, C>, Partitioner) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Combine elements of each key in DStream's RDDs using custom function.
- combineByKey(Function<V, C>, Function2<C, V, C>, Function2<C, C, C>, Partitioner, boolean) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Combine elements of each key in DStream's RDDs using custom function.
- combineByKey(Function1<V, C>, Function2<C, V, C>, Function2<C, C, C>, Partitioner, boolean, ClassTag<C>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Combine elements of each key in DStream's RDDs using custom functions.
- combineByKeyWithClassTag(Function1<V, C>, Function2<C, V, C>, Function2<C, C, C>, Partitioner, boolean, Serializer, ClassTag<C>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Generic function to combine the elements for each key using a custom set of aggregation
functions.
- combineByKeyWithClassTag(Function1<V, C>, Function2<C, V, C>, Function2<C, C, C>, int, ClassTag<C>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Simplified version of combineByKeyWithClassTag that hash-partitions the output RDD.
- combineByKeyWithClassTag(Function1<V, C>, Function2<C, V, C>, Function2<C, C, C>, ClassTag<C>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Simplified version of combineByKeyWithClassTag that hash-partitions the resulting RDD using the
existing partitioner/parallelism level.
- combineCombinersByKey(Iterator<? extends Product2<K, C>>, TaskContext) - 类 中的方法org.apache.spark.Aggregator
-
- combineValuesByKey(Iterator<? extends Product2<K, V>>, TaskContext) - 类 中的方法org.apache.spark.Aggregator
-
- CommandLineLoggingUtils - org.apache.spark.util中的接口
-
- CommandLineUtils - org.apache.spark.util中的接口
-
Contains basic command line parsing functionality and methods to parse some common Spark CLI
options.
- comment() - 类 中的方法org.apache.spark.sql.connector.catalog.TableChange.AddColumn
-
- commit(Function0<Parsers.Parser<T>>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- commit(Offset) - 接口 中的方法org.apache.spark.sql.connector.read.streaming.SparkDataStream
-
Informs the source that Spark has completed processing all data for offsets less than or
equal to `end` and will only request offsets greater than `end` in the future.
- commit(WriterCommitMessage[]) - 接口 中的方法org.apache.spark.sql.connector.write.BatchWrite
-
Commits this writing job with a list of commit messages.
- commit() - 接口 中的方法org.apache.spark.sql.connector.write.DataWriter
-
- commit(long, WriterCommitMessage[]) - 接口 中的方法org.apache.spark.sql.connector.write.streaming.StreamingWrite
-
Commits this writing job for the specified epoch with a list of commit messages.
- commitAllPartitions() - 接口 中的方法org.apache.spark.shuffle.api.ShuffleMapOutputWriter
-
- commitJob(JobContext, Seq<FileCommitProtocol.TaskCommitMessage>) - 类 中的方法org.apache.spark.internal.io.FileCommitProtocol
-
Commits a job after the writes succeed.
- commitJob(JobContext, Seq<FileCommitProtocol.TaskCommitMessage>) - 类 中的方法org.apache.spark.internal.io.HadoopMapReduceCommitProtocol
-
- commitStagedChanges() - 接口 中的方法org.apache.spark.sql.connector.catalog.StagedTable
-
Finalize the creation or replacement of this table.
- commitTask(TaskAttemptContext) - 类 中的方法org.apache.spark.internal.io.FileCommitProtocol
-
Commits a task after the writes succeed.
- commitTask(TaskAttemptContext) - 类 中的方法org.apache.spark.internal.io.HadoopMapReduceCommitProtocol
-
- commitTask(OutputCommitter, TaskAttemptContext, int, int) - 类 中的静态方法org.apache.spark.mapred.SparkHadoopMapRedUtil
-
Commits a task output.
- commonHeaderNodes(HttpServletRequest) - 类 中的静态方法org.apache.spark.ui.UIUtils
-
- comparator(Schedulable, Schedulable) - 接口 中的方法org.apache.spark.scheduler.SchedulingAlgorithm
-
- compare(PartitionGroup, PartitionGroup) - 类 中的方法org.apache.spark.rdd.DefaultPartitionCoalescer.partitionGroupOrdering$
-
- compare(byte, byte) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- compare(Decimal) - 类 中的方法org.apache.spark.sql.types.Decimal
-
- compare(Decimal, Decimal) - 接口 中的方法org.apache.spark.sql.types.Decimal.DecimalIsConflicted
-
- compare(Decimal, Decimal) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- compare(double, double) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- compare(float, float) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- compare(int, int) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- compare(long, long) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- compare(short, short) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- compare(RDDInfo) - 类 中的方法org.apache.spark.storage.RDDInfo
-
- compareTo(SparkShutdownHook) - 类 中的方法org.apache.spark.util.SparkShutdownHook
-
- compileValue(Object) - 类 中的静态方法org.apache.spark.sql.jdbc.DB2Dialect
-
- compileValue(Object) - 类 中的静态方法org.apache.spark.sql.jdbc.DerbyDialect
-
- compileValue(Object) - 类 中的方法org.apache.spark.sql.jdbc.JdbcDialect
-
Converts value to SQL expression.
- compileValue(Object) - 类 中的静态方法org.apache.spark.sql.jdbc.MsSqlServerDialect
-
- compileValue(Object) - 类 中的静态方法org.apache.spark.sql.jdbc.MySQLDialect
-
- compileValue(Object) - 类 中的静态方法org.apache.spark.sql.jdbc.NoopDialect
-
- compileValue(Object) - 类 中的静态方法org.apache.spark.sql.jdbc.OracleDialect
-
- compileValue(Object) - 类 中的静态方法org.apache.spark.sql.jdbc.PostgresDialect
-
- compileValue(Object) - 类 中的静态方法org.apache.spark.sql.jdbc.TeradataDialect
-
- Complete() - 类 中的静态方法org.apache.spark.sql.streaming.OutputMode
-
OutputMode in which all the rows in the streaming DataFrame/Dataset will be written
to the sink every time there are some updates.
- completed() - 类 中的方法org.apache.spark.status.api.v1.ApplicationAttemptInfo
-
- completedIndices() - 类 中的方法org.apache.spark.status.LiveJob
-
- completedIndices() - 类 中的方法org.apache.spark.status.LiveStage
-
- completedStages() - 类 中的方法org.apache.spark.status.LiveJob
-
- completedTasks() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- completedTasks() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- completedTasks() - 类 中的方法org.apache.spark.status.LiveJob
-
- completedTasks() - 类 中的方法org.apache.spark.status.LiveStage
-
- COMPLETION_TIME() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- completionTime() - 类 中的方法org.apache.spark.scheduler.StageInfo
-
Time when all tasks in the stage completed or when the stage was cancelled.
- completionTime() - 类 中的方法org.apache.spark.status.api.v1.JobData
-
- completionTime() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- completionTime() - 类 中的方法org.apache.spark.status.LiveJob
-
- ComplexFutureAction<T> - org.apache.spark中的类
-
A
FutureAction for actions that could trigger multiple Spark jobs.
- ComplexFutureAction(Function1<JobSubmitter, Future<T>>) - 类 的构造器org.apache.spark.ComplexFutureAction
-
- compressed() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Returns a matrix in dense column major, dense row major, sparse row major, or sparse column
major format, whichever uses less storage.
- compressed() - 接口 中的方法org.apache.spark.ml.linalg.Vector
-
Returns a vector in either dense or sparse format, whichever uses less storage.
- compressed() - 接口 中的方法org.apache.spark.mllib.linalg.Vector
-
Returns a vector in either dense or sparse format, whichever uses less storage.
- compressedColMajor() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Returns a matrix in dense or sparse column major format, whichever uses less storage.
- compressedContinuousInputStream(InputStream) - 接口 中的方法org.apache.spark.io.CompressionCodec
-
- compressedContinuousInputStream(InputStream) - 类 中的方法org.apache.spark.io.ZStdCompressionCodec
-
- compressedContinuousOutputStream(OutputStream) - 接口 中的方法org.apache.spark.io.CompressionCodec
-
- compressedInputStream(InputStream) - 接口 中的方法org.apache.spark.io.CompressionCodec
-
- compressedInputStream(InputStream) - 类 中的方法org.apache.spark.io.LZ4CompressionCodec
-
- compressedInputStream(InputStream) - 类 中的方法org.apache.spark.io.LZFCompressionCodec
-
- compressedInputStream(InputStream) - 类 中的方法org.apache.spark.io.SnappyCompressionCodec
-
- compressedInputStream(InputStream) - 类 中的方法org.apache.spark.io.ZStdCompressionCodec
-
- compressedOutputStream(OutputStream) - 接口 中的方法org.apache.spark.io.CompressionCodec
-
- compressedOutputStream(OutputStream) - 类 中的方法org.apache.spark.io.LZ4CompressionCodec
-
- compressedOutputStream(OutputStream) - 类 中的方法org.apache.spark.io.LZFCompressionCodec
-
- compressedOutputStream(OutputStream) - 类 中的方法org.apache.spark.io.SnappyCompressionCodec
-
- compressedOutputStream(OutputStream) - 类 中的方法org.apache.spark.io.ZStdCompressionCodec
-
- compressedRowMajor() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Returns a matrix in dense or sparse row major format, whichever uses less storage.
- CompressionCodec - org.apache.spark.io中的接口
-
:: DeveloperApi ::
CompressionCodec allows the customization of choosing different compression implementations
to be used in block storage.
- compute(Partition, TaskContext) - 类 中的方法org.apache.spark.api.r.BaseRRDD
-
- compute(Partition, TaskContext) - 类 中的方法org.apache.spark.graphx.EdgeRDD
-
- compute(Partition, TaskContext) - 类 中的方法org.apache.spark.graphx.VertexRDD
-
Provides the RDD[(VertexId, VD)] equivalent output.
- compute(Vector, double, Vector) - 类 中的方法org.apache.spark.mllib.optimization.Gradient
-
Compute the gradient and loss given the features of a single data point.
- compute(Vector, double, Vector, Vector) - 类 中的方法org.apache.spark.mllib.optimization.Gradient
-
Compute the gradient and loss given the features of a single data point,
add the gradient to a provided vector to avoid creating new objects, and return loss.
- compute(Vector, double, Vector) - 类 中的方法org.apache.spark.mllib.optimization.HingeGradient
-
- compute(Vector, double, Vector, Vector) - 类 中的方法org.apache.spark.mllib.optimization.HingeGradient
-
- compute(Vector, Vector, double, int, double) - 类 中的方法org.apache.spark.mllib.optimization.L1Updater
-
- compute(Vector, double, Vector) - 类 中的方法org.apache.spark.mllib.optimization.LeastSquaresGradient
-
- compute(Vector, double, Vector, Vector) - 类 中的方法org.apache.spark.mllib.optimization.LeastSquaresGradient
-
- compute(Vector, double, Vector, Vector) - 类 中的方法org.apache.spark.mllib.optimization.LogisticGradient
-
- compute(Vector, Vector, double, int, double) - 类 中的方法org.apache.spark.mllib.optimization.SimpleUpdater
-
- compute(Vector, Vector, double, int, double) - 类 中的方法org.apache.spark.mllib.optimization.SquaredL2Updater
-
- compute(Vector, Vector, double, int, double) - 类 中的方法org.apache.spark.mllib.optimization.Updater
-
Compute an updated value for weights given the gradient, stepSize, iteration number and
regularization parameter.
- compute(Partition, TaskContext) - 类 中的方法org.apache.spark.rdd.CoGroupedRDD
-
- compute(Partition, TaskContext) - 类 中的方法org.apache.spark.rdd.HadoopRDD
-
- compute(Partition, TaskContext) - 类 中的方法org.apache.spark.rdd.JdbcRDD
-
- compute(Partition, TaskContext) - 类 中的方法org.apache.spark.rdd.NewHadoopRDD
-
- compute(Partition, TaskContext) - 类 中的方法org.apache.spark.rdd.PartitionPruningRDD
-
- compute(Partition, TaskContext) - 类 中的方法org.apache.spark.rdd.RDD
-
:: DeveloperApi ::
Implemented by subclasses to compute a given partition.
- compute(Partition, TaskContext) - 类 中的方法org.apache.spark.rdd.ShuffledRDD
-
- compute(Partition, TaskContext) - 类 中的方法org.apache.spark.rdd.UnionRDD
-
- compute(Time) - 类 中的方法org.apache.spark.streaming.api.java.JavaDStream
-
Generate an RDD for the given duration
- compute(Time) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Method that generates an RDD for the given Duration
- compute(Time) - 类 中的方法org.apache.spark.streaming.dstream.ConstantInputDStream
-
- compute(Time) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Method that generates an RDD for the given time
- compute(Time) - 类 中的方法org.apache.spark.streaming.dstream.ReceiverInputDStream
-
- compute(long, long, long, long) - 接口 中的方法org.apache.spark.streaming.scheduler.rate.RateEstimator
-
Computes the number of records the stream attached to this RateEstimator
should ingest per second, given an update on the size and completion
times of the latest batch.
- computeClusterStats(Dataset<Row>, String, String) - 类 中的静态方法org.apache.spark.ml.evaluation.CosineSilhouette
-
The method takes the input dataset and computes the aggregated values
about a cluster which are needed by the algorithm.
- computeClusterStats(Dataset<Row>, String, String) - 类 中的静态方法org.apache.spark.ml.evaluation.SquaredEuclideanSilhouette
-
The method takes the input dataset and computes the aggregated values
about a cluster which are needed by the algorithm.
- computeColumnSummaryStatistics() - 类 中的方法org.apache.spark.mllib.linalg.distributed.RowMatrix
-
Computes column-wise summary statistics.
- computeCorrelation(RDD<Object>, RDD<Object>) - 接口 中的方法org.apache.spark.mllib.stat.correlation.Correlation
-
Compute correlation for two datasets.
- computeCorrelation(RDD<Object>, RDD<Object>) - 类 中的静态方法org.apache.spark.mllib.stat.correlation.PearsonCorrelation
-
Compute the Pearson correlation for two datasets.
- computeCorrelation(RDD<Object>, RDD<Object>) - 类 中的静态方法org.apache.spark.mllib.stat.correlation.SpearmanCorrelation
-
Compute Spearman's correlation for two datasets.
- computeCorrelationMatrix(RDD<Vector>) - 接口 中的方法org.apache.spark.mllib.stat.correlation.Correlation
-
Compute the correlation matrix S, for the input matrix, where S(i, j) is the correlation
between column i and j.
- computeCorrelationMatrix(RDD<Vector>) - 类 中的静态方法org.apache.spark.mllib.stat.correlation.PearsonCorrelation
-
Compute the Pearson correlation matrix S, for the input matrix, where S(i, j) is the
correlation between column i and j. 0 covariance results in a correlation value of Double.NaN.
- computeCorrelationMatrix(RDD<Vector>) - 类 中的静态方法org.apache.spark.mllib.stat.correlation.SpearmanCorrelation
-
Compute Spearman's correlation matrix S, for the input matrix, where S(i, j) is the
correlation between column i and j.
- computeCorrelationMatrixFromCovariance(Matrix) - 类 中的静态方法org.apache.spark.mllib.stat.correlation.PearsonCorrelation
-
Compute the Pearson correlation matrix from the covariance matrix.
0 variance results in a correlation value of Double.NaN.
- computeCorrelationWithMatrixImpl(RDD<Object>, RDD<Object>) - 接口 中的方法org.apache.spark.mllib.stat.correlation.Correlation
-
Combine the two input RDD[Double]s into an RDD[Vector] and compute the correlation using the
correlation implementation for RDD[Vector].
- computeCorrelationWithMatrixImpl(RDD<Object>, RDD<Object>) - 类 中的静态方法org.apache.spark.mllib.stat.correlation.PearsonCorrelation
-
- computeCorrelationWithMatrixImpl(RDD<Object>, RDD<Object>) - 类 中的静态方法org.apache.spark.mllib.stat.correlation.SpearmanCorrelation
-
- computeCost(Dataset<?>) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
- computeCost(Vector) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeansModel
-
Computes the squared distance between the input point and the cluster center it belongs to.
- computeCost(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeansModel
-
Computes the sum of squared distances between the input points and their corresponding cluster
centers.
- computeCost(JavaRDD<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeansModel
-
Java-friendly version of computeCost().
- computeCost(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.KMeansModel
-
Return the K-means cost (sum of squared distances of points to their nearest center) for this
model on the given data.
- computeCovariance() - 类 中的方法org.apache.spark.mllib.linalg.distributed.RowMatrix
-
Computes the covariance matrix, treating each row as an observation.
- computeError(org.apache.spark.mllib.tree.model.TreeEnsembleModel, RDD<LabeledPoint>) - 接口 中的方法org.apache.spark.mllib.tree.loss.Loss
-
Method to calculate error of the base learner for the gradient boosting calculation.
- computeError(double, double) - 接口 中的方法org.apache.spark.mllib.tree.loss.Loss
-
Method to calculate loss when the predictions are already known.
- computeFractionForSampleSize(int, long, boolean) - 类 中的静态方法org.apache.spark.util.random.SamplingUtils
-
Returns a sampling rate that guarantees a sample of size greater than or equal to
sampleSizeLowerBound 99.99% of the time.
- computeGradient(DenseMatrix<Object>, DenseMatrix<Object>, Vector, int) - 接口 中的方法org.apache.spark.ml.ann.TopologyModel
-
Computes gradient for the network
- computeGramianMatrix() - 类 中的方法org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
-
Computes the Gramian matrix A^T A.
- computeGramianMatrix() - 类 中的方法org.apache.spark.mllib.linalg.distributed.RowMatrix
-
Computes the Gramian matrix A^T A.
- computeInitialPredictionAndError(RDD<org.apache.spark.ml.feature.Instance>, double, DecisionTreeRegressionModel, Loss) - 类 中的静态方法org.apache.spark.ml.tree.impl.GradientBoostedTrees
-
Compute the initial predictions and errors for a dataset for the first
iteration of gradient boosting.
- computeInitialPredictionAndError(RDD<LabeledPoint>, double, DecisionTreeModel, Loss) - 类 中的静态方法org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
-
:: DeveloperApi ::
Compute the initial predictions and errors for a dataset for the first
iteration of gradient boosting.
- computePreferredLocations(Seq<InputFormatInfo>) - 类 中的静态方法org.apache.spark.scheduler.InputFormatInfo
-
Computes the preferred locations based on input(s) and returned a location to block map.
- computePrevDelta(DenseMatrix<Object>, DenseMatrix<Object>, DenseMatrix<Object>) - 接口 中的方法org.apache.spark.ml.ann.LayerModel
-
Computes the delta for back propagation.
- computePrincipalComponents(int) - 类 中的方法org.apache.spark.mllib.linalg.distributed.RowMatrix
-
Computes the top k principal components only.
- computePrincipalComponentsAndExplainedVariance(int) - 类 中的方法org.apache.spark.mllib.linalg.distributed.RowMatrix
-
Computes the top k principal components and a vector of proportions of
variance explained by each principal component.
- computeProbability(double) - 接口 中的方法org.apache.spark.mllib.tree.loss.ClassificationLoss
-
Computes the class probability given the margin.
- computeSilhouetteCoefficient(Broadcast<Map<Object, Tuple2<Vector, Object>>>, Vector, double) - 类 中的静态方法org.apache.spark.ml.evaluation.CosineSilhouette
-
It computes the Silhouette coefficient for a point.
- computeSilhouetteCoefficient(Broadcast<Map<Object, SquaredEuclideanSilhouette.ClusterStats>>, Vector, double, double) - 类 中的静态方法org.apache.spark.ml.evaluation.SquaredEuclideanSilhouette
-
It computes the Silhouette coefficient for a point.
- computeSilhouetteScore(Dataset<?>, String, String) - 类 中的静态方法org.apache.spark.ml.evaluation.CosineSilhouette
-
Compute the Silhouette score of the dataset using the cosine distance measure.
- computeSilhouetteScore(Dataset<?>, String, String) - 类 中的静态方法org.apache.spark.ml.evaluation.SquaredEuclideanSilhouette
-
Compute the Silhouette score of the dataset using squared Euclidean distance measure.
- computeSVD(int, boolean, double) - 类 中的方法org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
-
Computes the singular value decomposition of this IndexedRowMatrix.
- computeSVD(int, boolean, double) - 类 中的方法org.apache.spark.mllib.linalg.distributed.RowMatrix
-
Computes singular value decomposition of this matrix.
- computeThresholdByKey(Map<K, AcceptanceResult>, Map<K, Object>) - 类 中的静态方法org.apache.spark.util.random.StratifiedSamplingUtils
-
Given the result returned by getCounts, determine the threshold for accepting items to
generate exact sample size.
- computeWeightedError(RDD<org.apache.spark.ml.feature.Instance>, DecisionTreeRegressionModel[], double[], Loss) - 类 中的静态方法org.apache.spark.ml.tree.impl.GradientBoostedTrees
-
Method to calculate error of the base learner for the gradient boosting calculation.
- computeWeightedError(RDD<org.apache.spark.ml.feature.Instance>, RDD<Tuple2<Object, Object>>) - 类 中的静态方法org.apache.spark.ml.tree.impl.GradientBoostedTrees
-
Method to calculate error of the base learner for the gradient boosting calculation.
- concat(Column...) - 类 中的静态方法org.apache.spark.sql.functions
-
Concatenates multiple input columns together into a single column.
- concat(Seq<Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Concatenates multiple input columns together into a single column.
- concat_ws(String, Column...) - 类 中的静态方法org.apache.spark.sql.functions
-
Concatenates multiple input string columns together into a single string column,
using the given separator.
- concat_ws(String, Seq<Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Concatenates multiple input string columns together into a single string column,
using the given separator.
- Conf(int, int, double, double, double, double, double, double) - 类 的构造器org.apache.spark.graphx.lib.SVDPlusPlus.Conf
-
- conf() - 类 中的方法org.apache.spark.SparkEnv
-
- conf() - 类 中的方法org.apache.spark.sql.hive.RelationConversions
-
- conf() - 类 中的方法org.apache.spark.sql.SparkSession
-
- confidence() - 类 中的方法org.apache.spark.mllib.fpm.AssociationRules.Rule
-
Returns the confidence of the rule.
- confidence() - 类 中的方法org.apache.spark.partial.BoundedDouble
-
- confidence() - 类 中的方法org.apache.spark.util.sketch.CountMinSketch
-
- config(String, String) - 类 中的方法org.apache.spark.sql.SparkSession.Builder
-
Sets a config option.
- config(String, long) - 类 中的方法org.apache.spark.sql.SparkSession.Builder
-
Sets a config option.
- config(String, double) - 类 中的方法org.apache.spark.sql.SparkSession.Builder
-
Sets a config option.
- config(String, boolean) - 类 中的方法org.apache.spark.sql.SparkSession.Builder
-
Sets a config option.
- config(SparkConf) - 类 中的方法org.apache.spark.sql.SparkSession.Builder
-
Sets a list of config options based on the given SparkConf.
- ConfigEntryWithDefault<T> - org.apache.spark.internal.config中的类
-
- ConfigEntryWithDefault(String, Option<String>, String, List<String>, T, Function1<String, T>, Function1<T, String>, String, boolean) - 类 的构造器org.apache.spark.internal.config.ConfigEntryWithDefault
-
- ConfigEntryWithDefaultFunction<T> - org.apache.spark.internal.config中的类
-
- ConfigEntryWithDefaultFunction(String, Option<String>, String, List<String>, Function0<T>, Function1<String, T>, Function1<T, String>, String, boolean) - 类 的构造器org.apache.spark.internal.config.ConfigEntryWithDefaultFunction
-
- ConfigEntryWithDefaultString<T> - org.apache.spark.internal.config中的类
-
- ConfigEntryWithDefaultString(String, Option<String>, String, List<String>, String, Function1<String, T>, Function1<T, String>, String, boolean) - 类 的构造器org.apache.spark.internal.config.ConfigEntryWithDefaultString
-
- ConfigHelpers - org.apache.spark.internal.config中的类
-
- ConfigHelpers() - 类 的构造器org.apache.spark.internal.config.ConfigHelpers
-
- ConfigProvider - org.apache.spark.internal.config中的接口
-
A source of configuration values.
- configTestLog4j(String) - 类 中的静态方法org.apache.spark.TestUtils
-
config a log4j properties used for testsuite
- Configurable - org.apache.spark.input中的接口
-
A trait to implement Configurable interface.
- configuration() - 类 中的方法org.apache.spark.scheduler.InputFormatInfo
-
- CONFIGURATION_INSTANTIATION_LOCK() - 类 中的静态方法org.apache.spark.rdd.HadoopRDD
-
Configuration's constructor is not threadsafe (see SPARK-1097 and HADOOP-10456).
- CONFIGURATION_INSTANTIATION_LOCK() - 类 中的静态方法org.apache.spark.rdd.NewHadoopRDD
-
Configuration's constructor is not threadsafe (see SPARK-1097 and HADOOP-10456).
- configureJobPropertiesForStorageHandler(TableDesc, Configuration, boolean) - 类 中的静态方法org.apache.spark.sql.hive.HiveTableUtil
-
- confusionMatrix() - 类 中的方法org.apache.spark.mllib.evaluation.MulticlassMetrics
-
Returns confusion matrix:
predicted classes are in columns,
they are ordered by class label ascending,
as in "labels"
- connectedComponents() - 类 中的方法org.apache.spark.graphx.GraphOps
-
Compute the connected component membership of each vertex and return a graph with the vertex
value containing the lowest vertex id in the connected component containing that vertex.
- connectedComponents(int) - 类 中的方法org.apache.spark.graphx.GraphOps
-
Compute the connected component membership of each vertex and return a graph with the vertex
value containing the lowest vertex id in the connected component containing that vertex.
- ConnectedComponents - org.apache.spark.graphx.lib中的类
-
Connected components algorithm.
- ConnectedComponents() - 类 的构造器org.apache.spark.graphx.lib.ConnectedComponents
-
- consequent() - 类 中的方法org.apache.spark.mllib.fpm.AssociationRules.Rule
-
- ConstantInputDStream<T> - org.apache.spark.streaming.dstream中的类
-
An input stream that always returns the same RDD on each time step.
- ConstantInputDStream(StreamingContext, RDD<T>, ClassTag<T>) - 类 的构造器org.apache.spark.streaming.dstream.ConstantInputDStream
-
- constructTree(org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0.NodeData[]) - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$
-
Given a list of nodes from a tree, construct the tree.
- constructTrees(RDD<org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0.NodeData>) - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$
-
- contains(Param<?>) - 类 中的方法org.apache.spark.ml.param.ParamMap
-
Checks whether a parameter is explicitly specified.
- contains(String) - 类 中的方法org.apache.spark.SparkConf
-
Does the configuration contain a given parameter?
- contains(Object) - 类 中的方法org.apache.spark.sql.Column
-
Contains the other element.
- contains(String) - 类 中的方法org.apache.spark.sql.types.Metadata
-
Tests whether this Metadata contains a binding for a key.
- containsDelimiters() - 类 中的方法org.apache.spark.sql.hive.execution.HiveOptions
-
- containsKey(Object) - 类 中的方法org.apache.spark.api.java.JavaUtils.SerializableMapWrapper
-
- containsKey(Object) - 类 中的方法org.apache.spark.sql.util.CaseInsensitiveStringMap
-
- containsNull() - 类 中的方法org.apache.spark.sql.types.ArrayType
-
- containsValue(Object) - 类 中的方法org.apache.spark.sql.util.CaseInsensitiveStringMap
-
- contentType() - 类 中的方法org.apache.spark.ui.JettyUtils.ServletParams
-
- context() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
- context() - 类 中的方法org.apache.spark.InterruptibleIterator
-
- context() - 类 中的方法org.apache.spark.rdd.RDD
-
- context() - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
- context() - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return the StreamingContext associated with this DStream
- ContextBarrierId - org.apache.spark中的类
-
For each barrier stage attempt, only at most one barrier() call can be active at any time, thus
we can use (stageId, stageAttemptId) to identify the stage attempt where the barrier() call is
from.
- ContextBarrierId(int, int) - 类 的构造器org.apache.spark.ContextBarrierId
-
- Continuous() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.FeatureType
-
- Continuous(long) - 类 中的静态方法org.apache.spark.sql.streaming.Trigger
-
A trigger that continuously processes streaming data, asynchronously checkpointing at
the specified interval.
- Continuous(long, TimeUnit) - 类 中的静态方法org.apache.spark.sql.streaming.Trigger
-
A trigger that continuously processes streaming data, asynchronously checkpointing at
the specified interval.
{{{
import java.util.concurrent.TimeUnit
df.writeStream.trigger(Trigger.Continuous(10, TimeUnit.SECONDS))
}}}
- Continuous(Duration) - 类 中的静态方法org.apache.spark.sql.streaming.Trigger
-
(Scala-friendly)
A trigger that continuously processes streaming data, asynchronously checkpointing at
the specified interval.
{{{
import scala.concurrent.duration._
df.writeStream.trigger(Trigger.Continuous(10.seconds))
}}}
- Continuous(String) - 类 中的静态方法org.apache.spark.sql.streaming.Trigger
-
A trigger that continuously processes streaming data, asynchronously checkpointing at
the specified interval.
{{{
df.writeStream.trigger(Trigger.Continuous("10 seconds"))
}}}
- ContinuousPartitionReader<T> - org.apache.spark.sql.connector.read.streaming中的接口
-
A variation on
PartitionReader for use with continuous streaming processing.
- ContinuousPartitionReaderFactory - org.apache.spark.sql.connector.read.streaming中的接口
-
- ContinuousSplit - org.apache.spark.ml.tree中的类
-
Split which tests a continuous feature.
- ContinuousStream - org.apache.spark.sql.connector.read.streaming中的接口
-
- conv(Column, int, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Convert a number in a string column from one base to another.
- CONVERT_INSERTING_PARTITIONED_TABLE() - 类 中的静态方法org.apache.spark.sql.hive.HiveUtils
-
- CONVERT_METASTORE_CTAS() - 类 中的静态方法org.apache.spark.sql.hive.HiveUtils
-
- CONVERT_METASTORE_ORC() - 类 中的静态方法org.apache.spark.sql.hive.HiveUtils
-
- CONVERT_METASTORE_PARQUET() - 类 中的静态方法org.apache.spark.sql.hive.HiveUtils
-
- CONVERT_METASTORE_PARQUET_WITH_SCHEMA_MERGING() - 类 中的静态方法org.apache.spark.sql.hive.HiveUtils
-
- convertibleFilters(StructType, Map<String, DataType>, Seq<Filter>) - 类 中的静态方法org.apache.spark.sql.hive.orc.OrcFilters
-
- convertMatrixColumnsFromML(Dataset<?>, String...) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Converts matrix columns in an input Dataset to the
Matrix
type from the new
Matrix type under the
spark.ml package.
- convertMatrixColumnsFromML(Dataset<?>, Seq<String>) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Converts matrix columns in an input Dataset to the
Matrix
type from the new
Matrix type under the
spark.ml package.
- convertMatrixColumnsToML(Dataset<?>, String...) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Converts Matrix columns in an input Dataset from the
Matrix
type to the new
Matrix type under the
spark.ml package.
- convertMatrixColumnsToML(Dataset<?>, Seq<String>) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Converts Matrix columns in an input Dataset from the
Matrix
type to the new
Matrix type under the
spark.ml package.
- convertTableProperties(Map<String, String>, Map<String, String>, Option<String>, Option<String>, String) - 类 中的静态方法org.apache.spark.sql.connector.catalog.CatalogV2Util
-
- convertToCanonicalEdges(Function2<ED, ED, ED>) - 类 中的方法org.apache.spark.graphx.GraphOps
-
Convert bi-directional edges into uni-directional ones.
- convertToOldLossType(String) - 接口 中的方法org.apache.spark.ml.tree.GBTRegressorParams
-
- convertToTimeUnit(long, TimeUnit) - 类 中的静态方法org.apache.spark.streaming.ui.UIUtils
-
Convert milliseconds to the specified unit.
- convertVectorColumnsFromML(Dataset<?>, String...) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Converts vector columns in an input Dataset to the
Vector
type from the new
Vector type under the
spark.ml package.
- convertVectorColumnsFromML(Dataset<?>, Seq<String>) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Converts vector columns in an input Dataset to the
Vector
type from the new
Vector type under the
spark.ml package.
- convertVectorColumnsToML(Dataset<?>, String...) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Converts vector columns in an input Dataset from the
Vector
type to the new
Vector type under the
spark.ml package.
- convertVectorColumnsToML(Dataset<?>, Seq<String>) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Converts vector columns in an input Dataset from the
Vector
type to the new
Vector type under the
spark.ml package.
- CoordinateMatrix - org.apache.spark.mllib.linalg.distributed中的类
-
Represents a matrix in coordinate format.
- CoordinateMatrix(RDD<MatrixEntry>, long, long) - 类 的构造器org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
-
- CoordinateMatrix(RDD<MatrixEntry>) - 类 的构造器org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
-
Alternative constructor leaving matrix dimensions to be determined automatically.
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.classification.LinearSVC
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.classification.LinearSVCModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.classification.NaiveBayes
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.classification.NaiveBayesModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.classification.OneVsRest
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.classification.OneVsRestModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeans
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.clustering.DistributedLDAModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixture
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.clustering.KMeansModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.clustering.LocalLDAModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.clustering.PowerIterationClustering
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.Estimator
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.evaluation.ClusteringEvaluator
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.evaluation.Evaluator
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.evaluation.RankingEvaluator
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.evaluation.RegressionEvaluator
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.Binarizer
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.BucketedRandomProjectionLSH
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.BucketedRandomProjectionLSHModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.Bucketizer
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelectorModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.ColumnPruner
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.CountVectorizer
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.CountVectorizerModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.FeatureHasher
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.HashingTF
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.IDF
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.IDFModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.Imputer
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.ImputerModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.IndexToString
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.Interaction
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.MaxAbsScaler
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.MaxAbsScalerModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.MinHashLSH
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.MinHashLSHModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.MinMaxScaler
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.MinMaxScalerModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.OneHotEncoder
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.OneHotEncoderModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.PCA
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.PCAModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.PolynomialExpansion
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.QuantileDiscretizer
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.RegexTokenizer
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.RFormula
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.RFormulaModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.RobustScaler
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.RobustScalerModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.SQLTransformer
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.StandardScaler
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.StandardScalerModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.StopWordsRemover
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.StringIndexer
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.StringIndexerModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.Tokenizer
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.VectorAssembler
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.VectorAttributeRewriter
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.VectorIndexer
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.VectorIndexerModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.VectorSizeHint
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.VectorSlicer
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.feature.Word2VecModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.fpm.FPGrowth
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.fpm.FPGrowthModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.fpm.PrefixSpan
-
- copy(Vector, Vector) - 类 中的静态方法org.apache.spark.ml.linalg.BLAS
-
y = x
- copy() - 类 中的方法org.apache.spark.ml.linalg.DenseMatrix
-
- copy() - 类 中的方法org.apache.spark.ml.linalg.DenseVector
-
- copy() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Get a deep copy of the matrix.
- copy() - 类 中的方法org.apache.spark.ml.linalg.SparseMatrix
-
- copy() - 类 中的方法org.apache.spark.ml.linalg.SparseVector
-
- copy() - 接口 中的方法org.apache.spark.ml.linalg.Vector
-
Makes a deep copy of this vector.
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.Model
-
- copy() - 类 中的方法org.apache.spark.ml.param.ParamMap
-
Creates a copy of this param map.
- copy(ParamMap) - 接口 中的方法org.apache.spark.ml.param.Params
-
Creates a copy of this instance with the same UID and some extra params.
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.Pipeline
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.PipelineModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.PipelineStage
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.Predictor
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.recommendation.ALSModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.regression.IsotonicRegression
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.regression.IsotonicRegressionModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.regression.LinearRegressionModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.Transformer
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.tuning.CrossValidator
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.tuning.CrossValidatorModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplit
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplitModel
-
- copy(ParamMap) - 类 中的方法org.apache.spark.ml.UnaryTransformer
-
- copy(Vector, Vector) - 类 中的静态方法org.apache.spark.mllib.linalg.BLAS
-
y = x
- copy() - 类 中的方法org.apache.spark.mllib.linalg.DenseMatrix
-
- copy() - 类 中的方法org.apache.spark.mllib.linalg.DenseVector
-
- copy() - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
Get a deep copy of the matrix.
- copy() - 类 中的方法org.apache.spark.mllib.linalg.SparseMatrix
-
- copy() - 类 中的方法org.apache.spark.mllib.linalg.SparseVector
-
- copy() - 接口 中的方法org.apache.spark.mllib.linalg.Vector
-
Makes a deep copy of this vector.
- copy() - 类 中的方法org.apache.spark.mllib.random.ExponentialGenerator
-
- copy() - 类 中的方法org.apache.spark.mllib.random.GammaGenerator
-
- copy() - 类 中的方法org.apache.spark.mllib.random.LogNormalGenerator
-
- copy() - 类 中的方法org.apache.spark.mllib.random.PoissonGenerator
-
- copy() - 接口 中的方法org.apache.spark.mllib.random.RandomDataGenerator
-
Returns a copy of the RandomDataGenerator with a new instance of the rng object used in the
class when applicable for non-locking concurrent usage.
- copy() - 类 中的方法org.apache.spark.mllib.random.StandardNormalGenerator
-
- copy() - 类 中的方法org.apache.spark.mllib.random.UniformGenerator
-
- copy() - 类 中的方法org.apache.spark.mllib.random.WeibullGenerator
-
- copy() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
Returns a shallow copy of this instance.
- copy() - 接口 中的方法org.apache.spark.sql.Row
-
Make a copy of the current
Row object.
- copy() - 类 中的静态方法org.apache.spark.sql.sources.AlwaysFalse
-
- copy() - 类 中的静态方法org.apache.spark.sql.sources.AlwaysTrue
-
- copy() - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- copy() - 类 中的方法org.apache.spark.sql.vectorized.ColumnarMap
-
- copy() - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
Revisit this.
- copy() - 类 中的方法org.apache.spark.util.AccumulatorV2
-
Creates a new copy of this accumulator.
- copy() - 类 中的方法org.apache.spark.util.CollectionAccumulator
-
- copy() - 类 中的方法org.apache.spark.util.DoubleAccumulator
-
- copy() - 类 中的方法org.apache.spark.util.LongAccumulator
-
- copy() - 类 中的方法org.apache.spark.util.StatCounter
-
Clone this StatCounter
- copyAndReset() - 类 中的方法org.apache.spark.util.AccumulatorV2
-
Creates a new copy of this accumulator, which is zero value. i.e. call isZero on the copy
must return true.
- copyAndReset() - 类 中的方法org.apache.spark.util.CollectionAccumulator
-
- copyFileStreamNIO(FileChannel, WritableByteChannel, long, long) - 类 中的静态方法org.apache.spark.util.Utils
-
- copyStream(InputStream, OutputStream, boolean, boolean) - 类 中的静态方法org.apache.spark.util.Utils
-
Copy all data from an InputStream to an OutputStream.
- copyStreamUpTo(InputStream, long) - 类 中的静态方法org.apache.spark.util.Utils
-
Copy the first maxSize bytes of data from the InputStream to an in-memory
buffer, primarily to check for corruption.
- copyValues(T, ParamMap) - 接口 中的方法org.apache.spark.ml.param.Params
-
Copies param values from this instance to another instance for params shared by them.
- cores() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterExecutor
-
- coresGranted() - 类 中的方法org.apache.spark.status.api.v1.ApplicationInfo
-
- coresPerExecutor() - 类 中的方法org.apache.spark.status.api.v1.ApplicationInfo
-
- corr(Dataset<?>, String, String) - 类 中的静态方法org.apache.spark.ml.stat.Correlation
-
Compute the correlation matrix for the input Dataset of Vectors using the specified method.
- corr(Dataset<?>, String) - 类 中的静态方法org.apache.spark.ml.stat.Correlation
-
Compute the Pearson correlation matrix for the input Dataset of Vectors.
- corr(RDD<Object>, RDD<Object>, String) - 类 中的静态方法org.apache.spark.mllib.stat.correlation.Correlations
-
- corr(RDD<Vector>) - 类 中的静态方法org.apache.spark.mllib.stat.Statistics
-
Compute the Pearson correlation matrix for the input RDD of Vectors.
- corr(RDD<Vector>, String) - 类 中的静态方法org.apache.spark.mllib.stat.Statistics
-
Compute the correlation matrix for the input RDD of Vectors using the specified method.
- corr(RDD<Object>, RDD<Object>) - 类 中的静态方法org.apache.spark.mllib.stat.Statistics
-
Compute the Pearson correlation for the input RDDs.
- corr(JavaRDD<Double>, JavaRDD<Double>) - 类 中的静态方法org.apache.spark.mllib.stat.Statistics
-
Java-friendly version of corr()
- corr(RDD<Object>, RDD<Object>, String) - 类 中的静态方法org.apache.spark.mllib.stat.Statistics
-
Compute the correlation for the input RDDs using the specified method.
- corr(JavaRDD<Double>, JavaRDD<Double>, String) - 类 中的静态方法org.apache.spark.mllib.stat.Statistics
-
Java-friendly version of corr()
- corr(String, String, String) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
Calculates the correlation of two columns of a DataFrame.
- corr(String, String) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
- corr(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the Pearson Correlation Coefficient for two columns.
- corr(String, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the Pearson Correlation Coefficient for two columns.
- Correlation - org.apache.spark.ml.stat中的类
-
API for correlation functions in MLlib, compatible with DataFrames and Datasets.
- Correlation() - 类 的构造器org.apache.spark.ml.stat.Correlation
-
- Correlation - org.apache.spark.mllib.stat.correlation中的接口
-
Trait for correlation algorithms.
- CorrelationNames - org.apache.spark.mllib.stat.correlation中的类
-
Maintains supported and default correlation names.
- CorrelationNames() - 类 的构造器org.apache.spark.mllib.stat.correlation.CorrelationNames
-
- Correlations - org.apache.spark.mllib.stat.correlation中的类
-
Delegates computation to the specific correlation object based on the input method name.
- Correlations() - 类 的构造器org.apache.spark.mllib.stat.correlation.Correlations
-
- corrMatrix(RDD<Vector>, String) - 类 中的静态方法org.apache.spark.mllib.stat.correlation.Correlations
-
- cos(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
- cos(String) - 类 中的静态方法org.apache.spark.sql.functions
-
- cosh(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
- cosh(String) - 类 中的静态方法org.apache.spark.sql.functions
-
- CosineSilhouette - org.apache.spark.ml.evaluation中的类
-
The algorithm which is implemented in this object, instead, is an efficient and parallel
implementation of the Silhouette using the cosine distance measure.
- CosineSilhouette() - 类 的构造器org.apache.spark.ml.evaluation.CosineSilhouette
-
- count() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return the number of elements in the RDD.
- count() - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
The number of edges in the RDD.
- count() - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
The number of vertices in the RDD.
- count() - 类 中的方法org.apache.spark.ml.clustering.ExpectationAggregator
-
- count() - 类 中的方法org.apache.spark.ml.regression.AFTAggregator
-
- count(Column, Column) - 类 中的静态方法org.apache.spark.ml.stat.Summarizer
-
- count(Column) - 类 中的静态方法org.apache.spark.ml.stat.Summarizer
-
- count() - 类 中的方法org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
-
Sample size.
- count() - 接口 中的方法org.apache.spark.mllib.stat.MultivariateStatisticalSummary
-
Sample size.
- count() - 类 中的方法org.apache.spark.rdd.RDD
-
Return the number of elements in the RDD.
- count() - 类 中的方法org.apache.spark.sql.Dataset
-
Returns the number of rows in the Dataset.
- count(MapFunction<T, Object>) - 类 中的静态方法org.apache.spark.sql.expressions.javalang.typed
-
已过时。
Count aggregate function.
- count(Function1<IN, Object>) - 类 中的静态方法org.apache.spark.sql.expressions.scalalang.typed
-
已过时。
Count aggregate function.
- count(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the number of items in a group.
- count(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the number of items in a group.
- count() - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
Returns a
Dataset that contains a tuple with each key and the number of items present
for that key.
- count() - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Count the number of rows for each group.
- count() - 类 中的方法org.apache.spark.status.RDDPartitionSeq
-
- count() - 类 中的方法org.apache.spark.storage.ReadableChannelFileRegion
-
- count() - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD has a single element generated by counting each RDD
of this DStream.
- count() - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream in which each RDD has a single element generated by counting each RDD
of this DStream.
- count() - 类 中的方法org.apache.spark.util.DoubleAccumulator
-
Returns the number of elements added to the accumulator.
- count() - 类 中的方法org.apache.spark.util.LongAccumulator
-
Returns the number of elements added to the accumulator.
- count() - 类 中的方法org.apache.spark.util.StatCounter
-
- countApprox(long, double) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Approximate version of count() that returns a potentially incomplete result
within a timeout, even if not all tasks have finished.
- countApprox(long) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Approximate version of count() that returns a potentially incomplete result
within a timeout, even if not all tasks have finished.
- countApprox(long, double) - 类 中的方法org.apache.spark.rdd.RDD
-
Approximate version of count() that returns a potentially incomplete result
within a timeout, even if not all tasks have finished.
- countApproxDistinct(double) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return approximate number of distinct elements in the RDD.
- countApproxDistinct(int, int) - 类 中的方法org.apache.spark.rdd.RDD
-
Return approximate number of distinct elements in the RDD.
- countApproxDistinct(double) - 类 中的方法org.apache.spark.rdd.RDD
-
Return approximate number of distinct elements in the RDD.
- countApproxDistinctByKey(double, Partitioner) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return approximate number of distinct values for each key in this RDD.
- countApproxDistinctByKey(double, int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return approximate number of distinct values for each key in this RDD.
- countApproxDistinctByKey(double) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return approximate number of distinct values for each key in this RDD.
- countApproxDistinctByKey(int, int, Partitioner) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Return approximate number of distinct values for each key in this RDD.
- countApproxDistinctByKey(double, Partitioner) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Return approximate number of distinct values for each key in this RDD.
- countApproxDistinctByKey(double, int) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Return approximate number of distinct values for each key in this RDD.
- countApproxDistinctByKey(double) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Return approximate number of distinct values for each key in this RDD.
- countAsync() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
The asynchronous version of count, which returns a
future for counting the number of elements in this RDD.
- countAsync() - 类 中的方法org.apache.spark.rdd.AsyncRDDActions
-
Returns a future for counting the number of elements in the RDD.
- countByKey() - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Count the number of elements for each key, and return the result to the master as a Map.
- countByKey() - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Count the number of elements for each key, collecting the results to a local Map.
- countByKeyApprox(long) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Approximate version of countByKey that can return a partial result if it does
not finish within a timeout.
- countByKeyApprox(long, double) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Approximate version of countByKey that can return a partial result if it does
not finish within a timeout.
- countByKeyApprox(long, double) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Approximate version of countByKey that can return a partial result if it does
not finish within a timeout.
- countByValue() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return the count of each unique value in this RDD as a map of (value, count) pairs.
- countByValue(Ordering<T>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return the count of each unique value in this RDD as a local map of (value, count) pairs.
- countByValue() - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD contains the counts of each distinct value in
each RDD of this DStream.
- countByValue(int) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD contains the counts of each distinct value in
each RDD of this DStream.
- countByValue(int, Ordering<T>) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream in which each RDD contains the counts of each distinct value in
each RDD of this DStream.
- countByValueAndWindow(Duration, Duration) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD contains the count of distinct elements in
RDDs in a sliding window over this DStream.
- countByValueAndWindow(Duration, Duration, int) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD contains the count of distinct elements in
RDDs in a sliding window over this DStream.
- countByValueAndWindow(Duration, Duration, int, Ordering<T>) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream in which each RDD contains the count of distinct elements in
RDDs in a sliding window over this DStream.
- countByValueApprox(long, double) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Approximate version of countByValue().
- countByValueApprox(long) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Approximate version of countByValue().
- countByValueApprox(long, double, Ordering<T>) - 类 中的方法org.apache.spark.rdd.RDD
-
Approximate version of countByValue().
- countByWindow(Duration, Duration) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD has a single element generated by counting the number
of elements in a window over this DStream. windowDuration and slideDuration are as defined in
the window() operation.
- countByWindow(Duration, Duration) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream in which each RDD has a single element generated by counting the number
of elements in a sliding window over this DStream.
- countDistinct(Column, Column...) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the number of distinct items in a group.
- countDistinct(String, String...) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the number of distinct items in a group.
- countDistinct(Column, Seq<Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the number of distinct items in a group.
- countDistinct(String, Seq<String>) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the number of distinct items in a group.
- COUNTER() - 类 中的静态方法org.apache.spark.metrics.sink.StatsdMetricType
-
- CountingWritableChannel - org.apache.spark.storage中的类
-
- CountingWritableChannel(WritableByteChannel) - 类 的构造器org.apache.spark.storage.CountingWritableChannel
-
- countMinSketch(String, int, int, int) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
Builds a Count-min Sketch over a specified column.
- countMinSketch(String, double, double, int) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
Builds a Count-min Sketch over a specified column.
- countMinSketch(Column, int, int, int) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
Builds a Count-min Sketch over a specified column.
- countMinSketch(Column, double, double, int) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
Builds a Count-min Sketch over a specified column.
- CountMinSketch - org.apache.spark.util.sketch中的类
-
A Count-min sketch is a probabilistic data structure used for cardinality estimation using
sub-linear space.
- CountMinSketch() - 类 的构造器org.apache.spark.util.sketch.CountMinSketch
-
- CountMinSketch.Version - org.apache.spark.util.sketch中的枚举
-
- countTowardsTaskFailures() - 类 中的方法org.apache.spark.ExecutorLostFailure
-
- countTowardsTaskFailures() - 类 中的方法org.apache.spark.FetchFailed
-
Fetch failures lead to a different failure handling path: (1) we don't abort the stage after
4 task failures, instead we immediately go back to the stage which generated the map output,
and regenerate the missing data
- countTowardsTaskFailures() - 类 中的静态方法org.apache.spark.Resubmitted
-
- countTowardsTaskFailures() - 类 中的方法org.apache.spark.TaskCommitDenied
-
If a task failed because its attempt to commit was denied, do not count this failure
towards failing the stage.
- countTowardsTaskFailures() - 接口 中的方法org.apache.spark.TaskFailedReason
-
Whether this task failure should be counted towards the maximum number of times the task is
allowed to fail before the stage is aborted.
- countTowardsTaskFailures() - 类 中的方法org.apache.spark.TaskKilled
-
- countTowardsTaskFailures() - 类 中的静态方法org.apache.spark.TaskResultLost
-
- countTowardsTaskFailures() - 类 中的静态方法org.apache.spark.UnknownReason
-
- CountVectorizer - org.apache.spark.ml.feature中的类
-
- CountVectorizer(String) - 类 的构造器org.apache.spark.ml.feature.CountVectorizer
-
- CountVectorizer() - 类 的构造器org.apache.spark.ml.feature.CountVectorizer
-
- CountVectorizerModel - org.apache.spark.ml.feature中的类
-
Converts a text document to a sparse vector of token counts.
- CountVectorizerModel(String, String[]) - 类 的构造器org.apache.spark.ml.feature.CountVectorizerModel
-
- CountVectorizerModel(String[]) - 类 的构造器org.apache.spark.ml.feature.CountVectorizerModel
-
- CountVectorizerParams - org.apache.spark.ml.feature中的接口
-
- cov() - 类 中的方法org.apache.spark.ml.stat.distribution.MultivariateGaussian
-
- cov(String, String) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
Calculate the sample covariance of two numerical columns of a DataFrame.
- covar_pop(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the population covariance for two columns.
- covar_pop(String, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the population covariance for two columns.
- covar_samp(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the sample covariance for two columns.
- covar_samp(String, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the sample covariance for two columns.
- covs() - 类 中的方法org.apache.spark.ml.clustering.ExpectationAggregator
-
- crc32(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Calculates the cyclic redundancy check value (CRC32) of a binary column and
returns the value as a bigint.
- CreatableRelationProvider - org.apache.spark.sql.sources中的接口
-
- create(boolean, boolean, boolean, boolean, int) - 类 中的静态方法org.apache.spark.api.java.StorageLevels
-
Create a new StorageLevel object.
- create(JavaSparkContext, JdbcRDD.ConnectionFactory, String, long, long, int, Function<ResultSet, T>) - 类 中的静态方法org.apache.spark.rdd.JdbcRDD
-
Create an RDD that executes a SQL query on a JDBC connection and reads results.
- create(JavaSparkContext, JdbcRDD.ConnectionFactory, String, long, long, int) - 类 中的静态方法org.apache.spark.rdd.JdbcRDD
-
Create an RDD that executes a SQL query on a JDBC connection and reads results.
- create(RDD<T>, Function1<Object, Object>) - 类 中的静态方法org.apache.spark.rdd.PartitionPruningRDD
-
Create a PartitionPruningRDD.
- create(RpcEnvConfig) - 接口 中的方法org.apache.spark.rpc.RpcEnvFactory
-
- create() - 接口 中的方法org.apache.spark.sql.CreateTableWriter
-
Create a new table from the contents of the data frame.
- create() - 类 中的方法org.apache.spark.sql.DataFrameWriterV2
-
- create(Object...) - 类 中的静态方法org.apache.spark.sql.RowFactory
-
Create a
Row from the given arguments.
- create(long) - 类 中的静态方法org.apache.spark.util.sketch.BloomFilter
-
Creates a
BloomFilter with the expected number of insertions and a default expected
false positive probability of 3%.
- create(long, double) - 类 中的静态方法org.apache.spark.util.sketch.BloomFilter
-
Creates a
BloomFilter with the expected number of insertions and expected false
positive probability.
- create(long, long) - 类 中的静态方法org.apache.spark.util.sketch.BloomFilter
-
Creates a
BloomFilter with given
expectedNumItems and
numBits, it will
pick an optimal
numHashFunctions which can minimize
fpp for the bloom filter.
- create(int, int, int) - 类 中的静态方法org.apache.spark.util.sketch.CountMinSketch
-
- create(double, double, int) - 类 中的静态方法org.apache.spark.util.sketch.CountMinSketch
-
Creates a
CountMinSketch with given relative error (
eps),
confidence,
and random
seed.
- createAlterTable(Seq<String>, CatalogPlugin, Seq<String>, Seq<TableChange>) - 类 中的静态方法org.apache.spark.sql.connector.catalog.CatalogV2Util
-
- createArrayType(Column) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
- createArrayType(DataType) - 类 中的静态方法org.apache.spark.sql.types.DataTypes
-
Creates an ArrayType by specifying the data type of elements (elementType).
- createArrayType(DataType, boolean) - 类 中的静态方法org.apache.spark.sql.types.DataTypes
-
Creates an ArrayType by specifying the data type of elements (elementType) and
whether the array contains null values (containsNull).
- createAttrGroupForAttrNames(String, int, boolean, boolean) - 类 中的静态方法org.apache.spark.ml.feature.OneHotEncoderCommon
-
Creates an `AttributeGroup` with the required number of `BinaryAttribute`.
- createBatchWriterFactory() - 接口 中的方法org.apache.spark.sql.connector.write.BatchWrite
-
Creates a writer factory which will be serialized and sent to executors.
- createColumnarReader(InputPartition) - 接口 中的方法org.apache.spark.sql.connector.read.PartitionReaderFactory
-
Returns a columnar partition reader to read data from the given
InputPartition.
- createColumnarReader(InputPartition) - 接口 中的方法org.apache.spark.sql.connector.read.streaming.ContinuousPartitionReaderFactory
-
- createCombiner() - 类 中的方法org.apache.spark.Aggregator
-
- createCommitter(int) - 类 中的方法org.apache.spark.internal.io.HadoopWriteConfigUtil
-
- createCompiledClass(String, File, TestUtils.JavaSourceFromString, Seq<URL>) - 类 中的静态方法org.apache.spark.TestUtils
-
Creates a compiled class with the source file.
- createCompiledClass(String, File, String, String, Seq<URL>) - 类 中的静态方法org.apache.spark.TestUtils
-
Creates a compiled class with the given name.
- createContinuousReaderFactory() - 接口 中的方法org.apache.spark.sql.connector.read.streaming.ContinuousStream
-
- createCryptoInputStream(InputStream, SparkConf, byte[]) - 类 中的静态方法org.apache.spark.security.CryptoStreamUtils
-
Helper method to wrap InputStream with CryptoInputStream for decryption.
- createCryptoOutputStream(OutputStream, SparkConf, byte[]) - 类 中的静态方法org.apache.spark.security.CryptoStreamUtils
-
Helper method to wrap OutputStream with CryptoOutputStream for encryption.
- createDatabase(CatalogDatabase, boolean) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Creates a new database with the given name.
- createDataFrame(RDD<A>, TypeTags.TypeTag<A>) - 类 中的方法org.apache.spark.sql.SparkSession
-
Creates a DataFrame from an RDD of Product (e.g. case classes, tuples).
- createDataFrame(Seq<A>, TypeTags.TypeTag<A>) - 类 中的方法org.apache.spark.sql.SparkSession
-
Creates a DataFrame from a local Seq of Product.
- createDataFrame(RDD<Row>, StructType) - 类 中的方法org.apache.spark.sql.SparkSession
-
:: DeveloperApi ::
Creates a
DataFrame from an
RDD containing
Rows using the given schema.
- createDataFrame(JavaRDD<Row>, StructType) - 类 中的方法org.apache.spark.sql.SparkSession
-
:: DeveloperApi ::
Creates a
DataFrame from a
JavaRDD containing
Rows using the given schema.
- createDataFrame(List<Row>, StructType) - 类 中的方法org.apache.spark.sql.SparkSession
-
:: DeveloperApi ::
Creates a
DataFrame from a
java.util.List containing
Rows using the given schema.
- createDataFrame(RDD<?>, Class<?>) - 类 中的方法org.apache.spark.sql.SparkSession
-
Applies a schema to an RDD of Java Beans.
- createDataFrame(JavaRDD<?>, Class<?>) - 类 中的方法org.apache.spark.sql.SparkSession
-
Applies a schema to an RDD of Java Beans.
- createDataFrame(List<?>, Class<?>) - 类 中的方法org.apache.spark.sql.SparkSession
-
Applies a schema to a List of Java Beans.
- createDataFrame(RDD<A>, TypeTags.TypeTag<A>) - 类 中的方法org.apache.spark.sql.SQLContext
-
Creates a DataFrame from an RDD of Product (e.g. case classes, tuples).
- createDataFrame(Seq<A>, TypeTags.TypeTag<A>) - 类 中的方法org.apache.spark.sql.SQLContext
-
Creates a DataFrame from a local Seq of Product.
- createDataFrame(RDD<Row>, StructType) - 类 中的方法org.apache.spark.sql.SQLContext
-
:: DeveloperApi ::
Creates a
DataFrame from an
RDD containing
Rows using the given schema.
- createDataFrame(JavaRDD<Row>, StructType) - 类 中的方法org.apache.spark.sql.SQLContext
-
:: DeveloperApi ::
Creates a
DataFrame from a
JavaRDD containing
Rows using the given schema.
- createDataFrame(List<Row>, StructType) - 类 中的方法org.apache.spark.sql.SQLContext
-
:: DeveloperApi ::
Creates a
DataFrame from a
java.util.List containing
Rows using the given schema.
- createDataFrame(RDD<?>, Class<?>) - 类 中的方法org.apache.spark.sql.SQLContext
-
Applies a schema to an RDD of Java Beans.
- createDataFrame(JavaRDD<?>, Class<?>) - 类 中的方法org.apache.spark.sql.SQLContext
-
Applies a schema to an RDD of Java Beans.
- createDataFrame(List<?>, Class<?>) - 类 中的方法org.apache.spark.sql.SQLContext
-
Applies a schema to a List of Java Beans.
- createDataset(Seq<T>, Encoder<T>) - 类 中的方法org.apache.spark.sql.SparkSession
-
Creates a
Dataset from a local Seq of data of a given type.
- createDataset(RDD<T>, Encoder<T>) - 类 中的方法org.apache.spark.sql.SparkSession
-
Creates a
Dataset from an RDD of a given type.
- createDataset(List<T>, Encoder<T>) - 类 中的方法org.apache.spark.sql.SparkSession
-
Creates a
Dataset from a
java.util.List of a given type.
- createDataset(Seq<T>, Encoder<T>) - 类 中的方法org.apache.spark.sql.SQLContext
-
Creates a
Dataset from a local Seq of data of a given type.
- createDataset(RDD<T>, Encoder<T>) - 类 中的方法org.apache.spark.sql.SQLContext
-
Creates a
Dataset from an RDD of a given type.
- createDataset(List<T>, Encoder<T>) - 类 中的方法org.apache.spark.sql.SQLContext
-
Creates a
Dataset from a
java.util.List of a given type.
- createDecimalType(int, int) - 类 中的静态方法org.apache.spark.sql.types.DataTypes
-
Creates a DecimalType by specifying the precision and scale.
- createDecimalType() - 类 中的静态方法org.apache.spark.sql.types.DataTypes
-
Creates a DecimalType with default precision and scale, which are 10 and 0.
- createDF(RDD<byte[]>, StructType, SparkSession) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
- createDirectory(File) - 类 中的静态方法org.apache.spark.util.Utils
-
Create a directory given the abstract pathname
- createDirectory(String, String) - 类 中的静态方法org.apache.spark.util.Utils
-
Create a directory inside the given parent directory.
- createdTempDir() - 类 中的方法org.apache.spark.sql.hive.execution.InsertIntoHiveDirCommand
-
- createdTempDir() - 类 中的方法org.apache.spark.sql.hive.execution.InsertIntoHiveTable
-
- createdTempDir() - 接口 中的方法org.apache.spark.sql.hive.execution.SaveAsHiveFile
-
- createFilter(StructType, Filter[]) - 类 中的静态方法org.apache.spark.sql.hive.orc.OrcFilters
-
- createFunction(String, CatalogFunction) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Create a function in an existing database.
- createGlobalTempView(String) - 类 中的方法org.apache.spark.sql.Dataset
-
Creates a global temporary view using the given name.
- CreateHiveTableAsSelectBase - org.apache.spark.sql.hive.execution中的接口
-
- CreateHiveTableAsSelectCommand - org.apache.spark.sql.hive.execution中的类
-
Create table and insert the query result into it.
- CreateHiveTableAsSelectCommand(CatalogTable, LogicalPlan, Seq<String>, SaveMode) - 类 的构造器org.apache.spark.sql.hive.execution.CreateHiveTableAsSelectCommand
-
- createJar(Seq<File>, File, Option<String>, Option<String>) - 类 中的静态方法org.apache.spark.TestUtils
-
Create a jar file that contains this set of files.
- createJarWithClasses(Seq<String>, String, Seq<Tuple2<String, String>>, Seq<URL>) - 类 中的静态方法org.apache.spark.TestUtils
-
Create a jar that defines classes with the given names.
- createJarWithFiles(Map<String, String>, File) - 类 中的静态方法org.apache.spark.TestUtils
-
Create a jar file containing multiple files.
- createJobContext(String, int) - 类 中的方法org.apache.spark.internal.io.HadoopWriteConfigUtil
-
- createJobID(Date, int) - 类 中的静态方法org.apache.spark.internal.io.SparkHadoopWriterUtils
-
- createJobTrackerID(Date) - 类 中的静态方法org.apache.spark.internal.io.SparkHadoopWriterUtils
-
- createKey(SparkConf) - 类 中的静态方法org.apache.spark.security.CryptoStreamUtils
-
Creates a new encryption key.
- createListeners(SparkConf, ElementTrackingStore) - 接口 中的方法org.apache.spark.status.AppHistoryServerPlugin
-
Creates listeners to replay the event logs.
- createLogForDriver(SparkConf, String, Configuration) - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
Create a WriteAheadLog for the driver.
- createLogForReceiver(SparkConf, String, Configuration) - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
Create a WriteAheadLog for the receiver.
- createMapOutputWriter(int, long, int) - 接口 中的方法org.apache.spark.shuffle.api.ShuffleExecutorComponents
-
Called once per map task to create a writer that will be responsible for persisting all the
partitioned bytes written by that map task.
- createMapType(DataType, DataType) - 类 中的静态方法org.apache.spark.sql.types.DataTypes
-
Creates a MapType by specifying the data type of keys (keyType) and values
(keyType).
- createMapType(DataType, DataType, boolean) - 类 中的静态方法org.apache.spark.sql.types.DataTypes
-
Creates a MapType by specifying the data type of keys (keyType), the data type of
values (keyType), and whether values contain any null value
(valueContainsNull).
- createMetrics(long, long, long, long, long, long, long, long, long, long, long, long, long, long, long, long, long, long, long, long, long, long, long, long) - 类 中的静态方法org.apache.spark.status.LiveEntityHelpers
-
- createMetrics(long) - 类 中的静态方法org.apache.spark.status.LiveEntityHelpers
-
- createModel(DenseVector<Object>) - 接口 中的方法org.apache.spark.ml.ann.Layer
-
Returns the instance of the layer based on weights provided.
- createNamespace(String[], Map<String, String>) - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- createNamespace(String[], Map<String, String>) - 接口 中的方法org.apache.spark.sql.connector.catalog.SupportsNamespaces
-
Create a namespace in the catalog.
- createOrReplace() - 接口 中的方法org.apache.spark.sql.CreateTableWriter
-
Create a new table or replace an existing table with the contents of the data frame.
- createOrReplace() - 类 中的方法org.apache.spark.sql.DataFrameWriterV2
-
- createOrReplaceGlobalTempView(String) - 类 中的方法org.apache.spark.sql.Dataset
-
Creates or replaces a global temporary view using the given name.
- createOrReplaceTempView(String) - 类 中的方法org.apache.spark.sql.Dataset
-
Creates a local temporary view using the given name.
- createOutputOperationFailureForUI(String) - 类 中的静态方法org.apache.spark.streaming.ui.UIUtils
-
- createPartitions(String, String, Seq<CatalogTablePartition>, boolean) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Create one or many partitions in the given table.
- createPathFromString(String, JobConf) - 类 中的静态方法org.apache.spark.internal.io.SparkHadoopWriterUtils
-
- createPMMLModelExport(Object) - 类 中的静态方法org.apache.spark.mllib.pmml.export.PMMLModelExportFactory
-
Factory object to help creating the necessary PMMLModelExport implementation
taking as input the machine learning model (for example KMeansModel).
- createProxyHandler(Function1<String, Option<String>>) - 类 中的静态方法org.apache.spark.ui.JettyUtils
-
Create a handler for proxying request to Workers and Application Drivers
- createProxyLocationHeader(String, HttpServletRequest, URI) - 类 中的静态方法org.apache.spark.ui.JettyUtils
-
- createProxyURI(String, String, String, String) - 类 中的静态方法org.apache.spark.ui.JettyUtils
-
- createRDDFromArray(JavaSparkContext, byte[][]) - 类 中的静态方法org.apache.spark.api.r.RRDD
-
Create an RRDD given a sequence of byte arrays.
- createRDDFromFile(JavaSparkContext, String, int) - 类 中的静态方法org.apache.spark.api.r.RRDD
-
Create an RRDD given a temporary file name.
- createReadableChannel(ReadableByteChannel, SparkConf, byte[]) - 类 中的静态方法org.apache.spark.security.CryptoStreamUtils
-
Wrap a ReadableByteChannel for decryption.
- createReader(InputPartition) - 接口 中的方法org.apache.spark.sql.connector.read.PartitionReaderFactory
-
Returns a row-based partition reader to read data from the given
InputPartition.
- createReader(InputPartition) - 接口 中的方法org.apache.spark.sql.connector.read.streaming.ContinuousPartitionReaderFactory
-
- createReaderFactory() - 接口 中的方法org.apache.spark.sql.connector.read.Batch
-
- createReaderFactory() - 接口 中的方法org.apache.spark.sql.connector.read.streaming.MicroBatchStream
-
- createRedirectHandler(String, String, Function1<HttpServletRequest, BoxedUnit>, String, Set<String>) - 类 中的静态方法org.apache.spark.ui.JettyUtils
-
Create a handler that always redirects the user to the given path
- createRelation(SQLContext, SaveMode, Map<String, String>, Dataset<Row>) - 接口 中的方法org.apache.spark.sql.sources.CreatableRelationProvider
-
Saves a DataFrame to a destination (using data source-specific parameters)
- createRelation(SQLContext, Map<String, String>) - 接口 中的方法org.apache.spark.sql.sources.RelationProvider
-
Returns a new base relation with the given parameters.
- createRelation(SQLContext, Map<String, String>, StructType) - 接口 中的方法org.apache.spark.sql.sources.SchemaRelationProvider
-
Returns a new base relation with the given parameters and user defined schema.
- createSchedulerBackend(SparkContext, String, TaskScheduler) - 接口 中的方法org.apache.spark.scheduler.ExternalClusterManager
-
Create a scheduler backend for the given SparkContext and scheduler.
- createSecret(SparkConf) - 类 中的静态方法org.apache.spark.util.Utils
-
- createServletHandler(String, JettyUtils.ServletParams<T>, SparkConf, String) - 类 中的静态方法org.apache.spark.ui.JettyUtils
-
Create a context handler that responds to a request with the given path prefix
- createServletHandler(String, HttpServlet, String) - 类 中的静态方法org.apache.spark.ui.JettyUtils
-
Create a context handler that responds to a request with the given path prefix
- createSingleFileMapOutputWriter(int, long) - 接口 中的方法org.apache.spark.shuffle.api.ShuffleExecutorComponents
-
An optional extension for creating a map output writer that can optimize the transfer of a
single partition file, as the entire result of a map task, to the backing store.
- createSink(SQLContext, Map<String, String>, Seq<String>, OutputMode) - 接口 中的方法org.apache.spark.sql.sources.StreamSinkProvider
-
- createSource(SQLContext, String, Option<StructType>, String, Map<String, String>) - 接口 中的方法org.apache.spark.sql.sources.StreamSourceProvider
-
- createSparkContext(String, String, String, String[], Map<Object, Object>, Map<Object, Object>) - 类 中的静态方法org.apache.spark.api.r.RRDD
-
- createStaticHandler(String, String) - 类 中的静态方法org.apache.spark.ui.JettyUtils
-
Create a handler for serving files from a static directory
- createStream(JavaStreamingContext, String, String, String, String, int, Duration, StorageLevel, String, String, String, String, String) - 类 中的方法org.apache.spark.streaming.kinesis.KinesisUtilsPythonHelper
-
- createStreamingWriterFactory() - 接口 中的方法org.apache.spark.sql.connector.write.streaming.StreamingWrite
-
Creates a writer factory which will be serialized and sent to executors.
- createStructField(String, String, boolean) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
- createStructField(String, DataType, boolean, Metadata) - 类 中的静态方法org.apache.spark.sql.types.DataTypes
-
Creates a StructField by specifying the name (name), data type (dataType) and
whether values of this field can be null values (nullable).
- createStructField(String, DataType, boolean) - 类 中的静态方法org.apache.spark.sql.types.DataTypes
-
Creates a StructField with empty metadata.
- createStructType(Seq<StructField>) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
- createStructType(List<StructField>) - 类 中的静态方法org.apache.spark.sql.types.DataTypes
-
Creates a StructType with the given list of StructFields (fields).
- createStructType(StructField[]) - 类 中的静态方法org.apache.spark.sql.types.DataTypes
-
Creates a StructType with the given StructField array (fields).
- createTable(String, String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Creates a table from the given path and returns the corresponding DataFrame.
- createTable(String, String, String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Creates a table from the given path based on a data source and returns the corresponding
DataFrame.
- createTable(String, String, Map<String, String>) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Creates a table based on the dataset in a data source and a set of options.
- createTable(String, String, Map<String, String>) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
(Scala-specific)
Creates a table based on the dataset in a data source and a set of options.
- createTable(String, String, StructType, Map<String, String>) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Create a table based on the dataset in a data source, a schema and a set of options.
- createTable(String, String, StructType, Map<String, String>) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
(Scala-specific)
Create a table based on the dataset in a data source, a schema and a set of options.
- createTable(Identifier, StructType, Transform[], Map<String, String>) - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- createTable(Identifier, StructType, Transform[], Map<String, String>) - 接口 中的方法org.apache.spark.sql.connector.catalog.TableCatalog
-
Create a table in the catalog.
- createTable(CatalogTable, boolean) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Creates a table with the given metadata.
- CreateTableWriter<T> - org.apache.spark.sql中的接口
-
Trait to restrict calls to create and replace operations.
- createTaskAttemptContext(String, int, int, int) - 类 中的方法org.apache.spark.internal.io.HadoopWriteConfigUtil
-
- createTaskScheduler(SparkContext, String) - 接口 中的方法org.apache.spark.scheduler.ExternalClusterManager
-
Create a task scheduler instance for the given SparkContext
- createTempDir(String, String) - 类 中的静态方法org.apache.spark.util.Utils
-
Create a temporary directory inside the given parent directory.
- createTempJsonFile(File, String, JsonAST.JValue) - 类 中的静态方法org.apache.spark.TestUtils
-
Creates a temp JSON file that contains the input JSON record.
- createTempScriptWithExpectedOutput(File, String, String) - 类 中的静态方法org.apache.spark.TestUtils
-
Creates a temp bash script that prints the given output.
- createTempView(String) - 类 中的方法org.apache.spark.sql.Dataset
-
Creates a local temporary view using the given name.
- createUnsafe(long, int, int) - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
Creates a decimal from unscaled, precision and scale without checking the bounds.
- createWorkspace(int) - 类 中的静态方法org.apache.spark.mllib.optimization.NNLS
-
- createWritableChannel(WritableByteChannel, SparkConf, byte[]) - 类 中的静态方法org.apache.spark.security.CryptoStreamUtils
-
Wrap a WritableByteChannel for encryption.
- createWriter(int, long) - 接口 中的方法org.apache.spark.sql.connector.write.DataWriterFactory
-
Returns a data writer to do the actual writing work.
- createWriter(int, long, long) - 接口 中的方法org.apache.spark.sql.connector.write.streaming.StreamingDataWriterFactory
-
Returns a data writer to do the actual writing work.
- crossJoin(Dataset<?>) - 类 中的方法org.apache.spark.sql.Dataset
-
Explicit cartesian join with another DataFrame.
- crosstab(String, String) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
Computes a pair-wise frequency table of the given columns.
- CrossValidator - org.apache.spark.ml.tuning中的类
-
K-fold cross validation performs model selection by splitting the dataset into a set of
non-overlapping randomly partitioned folds which are used as separate training and test datasets
e.g., with k=3 folds, K-fold cross validation will generate 3 (training, test) dataset pairs,
each of which uses 2/3 of the data for training and 1/3 for testing.
- CrossValidator(String) - 类 的构造器org.apache.spark.ml.tuning.CrossValidator
-
- CrossValidator() - 类 的构造器org.apache.spark.ml.tuning.CrossValidator
-
- CrossValidatorModel - org.apache.spark.ml.tuning中的类
-
CrossValidatorModel contains the model with the highest average cross-validation
metric across folds and uses this model to transform input data.
- CrossValidatorModel.CrossValidatorModelWriter - org.apache.spark.ml.tuning中的类
-
Writer for CrossValidatorModel.
- CrossValidatorParams - org.apache.spark.ml.tuning中的接口
-
- CryptoStreamUtils - org.apache.spark.security中的类
-
A util class for manipulating IO encryption and decryption streams.
- CryptoStreamUtils() - 类 的构造器org.apache.spark.security.CryptoStreamUtils
-
- CryptoStreamUtils.BaseErrorHandler - org.apache.spark.security中的接口
-
SPARK-25535.
- CryptoStreamUtils.ErrorHandlingReadableChannel - org.apache.spark.security中的类
-
- csv(String...) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads CSV files and returns the result as a DataFrame.
- csv(String) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads a CSV file and returns the result as a DataFrame.
- csv(Dataset<String>) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads an Dataset[String] storing CSV rows and returns the result as a DataFrame.
- csv(Seq<String>) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads CSV files and returns the result as a DataFrame.
- csv(String) - 类 中的方法org.apache.spark.sql.DataFrameWriter
-
Saves the content of the DataFrame in CSV format at the specified path.
- csv(String) - 类 中的方法org.apache.spark.sql.streaming.DataStreamReader
-
Loads a CSV file stream and returns the result as a DataFrame.
- cube(Column...) - 类 中的方法org.apache.spark.sql.Dataset
-
Create a multi-dimensional cube for the current Dataset using the specified columns,
so we can run aggregation on them.
- cube(String, String...) - 类 中的方法org.apache.spark.sql.Dataset
-
Create a multi-dimensional cube for the current Dataset using the specified columns,
so we can run aggregation on them.
- cube(Seq<Column>) - 类 中的方法org.apache.spark.sql.Dataset
-
Create a multi-dimensional cube for the current Dataset using the specified columns,
so we can run aggregation on them.
- cube(String, Seq<String>) - 类 中的方法org.apache.spark.sql.Dataset
-
Create a multi-dimensional cube for the current Dataset using the specified columns,
so we can run aggregation on them.
- CubeType$() - 类 的构造器org.apache.spark.sql.RelationalGroupedDataset.CubeType$
-
- cume_dist() - 类 中的静态方法org.apache.spark.sql.functions
-
Window function: returns the cumulative distribution of values within a window partition,
i.e. the fraction of rows that are below the current row.
- curId() - 类 中的静态方法org.apache.spark.sql.Dataset
-
- current_date() - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the current date as a date column.
- current_timestamp() - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the current timestamp as a timestamp column.
- currentAttemptId() - 接口 中的方法org.apache.spark.SparkStageInfo
-
- currentAttemptId() - 类 中的方法org.apache.spark.SparkStageInfoImpl
-
- currentCatalog() - 接口 中的方法org.apache.spark.sql.connector.catalog.LookupCatalog
-
Returns the current catalog set.
- currentDatabase() - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Returns the current default database in this session.
- currentResult() - 接口 中的方法org.apache.spark.partial.ApproximateEvaluator
-
- currentRow() - 类 中的静态方法org.apache.spark.sql.expressions.Window
-
Value representing the current row.
- currPrefLocs(Partition, RDD<?>) - 类 中的方法org.apache.spark.rdd.DefaultPartitionCoalescer
-
- CUSTOM_EXECUTOR_LOG_URL() - 类 中的静态方法org.apache.spark.internal.config.History
-
- CUSTOM_EXECUTOR_LOG_URL() - 类 中的静态方法org.apache.spark.internal.config.UI
-
- customMetrics() - 类 中的方法org.apache.spark.sql.streaming.StateOperatorProgress
-
- DAGSchedulerEvent - org.apache.spark.scheduler中的接口
-
Types of events that can be handled by the DAGScheduler.
- dapply(Dataset<Row>, byte[], byte[], Object[], StructType) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
The helper function for dapply() on R side.
- Data(Vector, double, Option<Object>) - 类 的构造器org.apache.spark.mllib.classification.impl.GLMClassificationModel.SaveLoadV1_0$.Data
-
- Data(double[], double[], double[][]) - 类 的构造器org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV1_0$.Data
-
- Data(double[], double[], double[][], String) - 类 的构造器org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV2_0$.Data
-
- Data(int) - 类 的构造器org.apache.spark.mllib.feature.ChiSqSelectorModel.SaveLoadV1_0$.Data
-
- Data(Vector, double) - 类 的构造器org.apache.spark.mllib.regression.impl.GLMRegressionModel.SaveLoadV1_0$.Data
-
- data() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.LaunchTask
-
- data() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.StatusUpdate
-
- data() - 类 中的方法org.apache.spark.storage.ShuffleFetchCompletionListener
-
- Data$() - 类 的构造器org.apache.spark.mllib.classification.impl.GLMClassificationModel.SaveLoadV1_0$.Data$
-
- Data$() - 类 的构造器org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV1_0$.Data$
-
- Data$() - 类 的构造器org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV2_0$.Data$
-
- Data$() - 类 的构造器org.apache.spark.mllib.feature.ChiSqSelectorModel.SaveLoadV1_0$.Data$
-
- Data$() - 类 的构造器org.apache.spark.mllib.regression.impl.GLMRegressionModel.SaveLoadV1_0$.Data$
-
- Database - org.apache.spark.sql.catalog中的类
-
A database in Spark, as returned by the
listDatabases method defined in
Catalog.
- Database(String, String, String) - 类 的构造器org.apache.spark.sql.catalog.Database
-
- database() - 类 中的方法org.apache.spark.sql.catalog.Function
-
- database() - 类 中的方法org.apache.spark.sql.catalog.Table
-
- databaseExists(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Check if the database with the specified name exists.
- databaseExists(String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Return whether a table/view with the specified name exists.
- databaseTypeDefinition() - 类 中的方法org.apache.spark.sql.jdbc.JdbcType
-
- dataDistribution() - 类 中的方法org.apache.spark.status.api.v1.RDDStorageInfo
-
- DATAFRAME_DAPPLY() - 类 中的静态方法org.apache.spark.api.r.RRunnerModes
-
- DATAFRAME_GAPPLY() - 类 中的静态方法org.apache.spark.api.r.RRunnerModes
-
- DataFrameNaFunctions - org.apache.spark.sql中的类
-
Functionality for working with missing data in DataFrames.
- DataFrameReader - org.apache.spark.sql中的类
-
Interface used to load a
Dataset from external storage systems (e.g. file systems,
key-value stores, etc).
- DataFrameStatFunctions - org.apache.spark.sql中的类
-
Statistic functions for DataFrames.
- DataFrameWriter<T> - org.apache.spark.sql中的类
-
Interface used to write a
Dataset to external storage systems (e.g. file systems,
key-value stores, etc).
- DataFrameWriterV2<T> - org.apache.spark.sql中的类
-
Interface used to write a
Dataset to external storage using the v2 API.
- dataset() - 类 中的方法org.apache.spark.ml.FitStart
-
- Dataset<T> - org.apache.spark.sql中的类
-
A Dataset is a strongly typed collection of domain-specific objects that can be transformed
in parallel using functional or relational operations.
- Dataset(SparkSession, LogicalPlan, Encoder<T>) - 类 的构造器org.apache.spark.sql.Dataset
-
- Dataset(SQLContext, LogicalPlan, Encoder<T>) - 类 的构造器org.apache.spark.sql.Dataset
-
- DATASET_ID_KEY() - 类 中的静态方法org.apache.spark.sql.Dataset
-
- DATASET_ID_TAG() - 类 中的静态方法org.apache.spark.sql.Dataset
-
- DatasetHolder<T> - org.apache.spark.sql中的类
-
A container for a
Dataset, used for implicit conversions in Scala.
- DatasetUtils - org.apache.spark.ml.util中的类
-
- DatasetUtils() - 类 的构造器org.apache.spark.ml.util.DatasetUtils
-
- dataSource() - 接口 中的方法org.apache.spark.ui.PagedTable
-
- DataSourceRegister - org.apache.spark.sql.sources中的接口
-
Data sources should implement this trait so that they can register an alias to their data source.
- DataStreamReader - org.apache.spark.sql.streaming中的类
-
Interface used to load a streaming Dataset from external storage systems (e.g. file systems,
key-value stores, etc).
- DataStreamWriter<T> - org.apache.spark.sql.streaming中的类
-
Interface used to write a streaming Dataset to external storage systems (e.g. file systems,
key-value stores, etc).
- dataTablesHeaderNodes(HttpServletRequest) - 类 中的静态方法org.apache.spark.ui.UIUtils
-
- dataType() - 类 中的方法org.apache.spark.sql.catalog.Column
-
- dataType() - 类 中的方法org.apache.spark.sql.connector.catalog.TableChange.AddColumn
-
- dataType() - 接口 中的方法org.apache.spark.sql.connector.expressions.Literal
-
Returns the SQL data type of the literal.
- dataType() - 类 中的方法org.apache.spark.sql.expressions.UserDefinedAggregateFunction
-
- DataType - org.apache.spark.sql.types中的类
-
The base type of all Spark SQL data types.
- DataType() - 类 的构造器org.apache.spark.sql.types.DataType
-
- dataType() - 类 中的方法org.apache.spark.sql.types.StructField
-
- dataType() - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Returns the data type of this column vector.
- DataTypes - org.apache.spark.sql.types中的类
-
To get/create specific data type, users should use singleton objects and factory methods
provided by this class.
- DataTypes() - 类 的构造器org.apache.spark.sql.types.DataTypes
-
- DataValidators - org.apache.spark.mllib.util中的类
-
:: DeveloperApi ::
A collection of methods used to validate data before applying ML algorithms.
- DataValidators() - 类 的构造器org.apache.spark.mllib.util.DataValidators
-
- DataWriter<T> - org.apache.spark.sql.connector.write中的接口
-
- DataWriterFactory - org.apache.spark.sql.connector.write中的接口
-
- date() - 类 中的方法org.apache.spark.sql.ColumnName
-
Creates a new StructField of type date.
- DATE() - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for nullable date type.
- date_add(Column, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the date that is days days after start
- date_add(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the date that is days days after start
- date_format(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Converts a date/timestamp/string to a value of string in the format specified by the date
format given by the second argument.
- date_sub(Column, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the date that is days days before start
- date_sub(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the date that is days days before start
- date_trunc(String, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns timestamp truncated to the unit specified by the format.
- datediff(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the number of days from start to end.
- DateType - 类 中的静态变量org.apache.spark.sql.types.DataTypes
-
Gets the DateType object.
- DateType - org.apache.spark.sql.types中的类
-
The date type represents a valid date in the proleptic Gregorian calendar.
- DateType() - 类 的构造器org.apache.spark.sql.types.DateType
-
- dayofmonth(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Extracts the day of the month as an integer from a given date/timestamp/string.
- dayofweek(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Extracts the day of the week as an integer from a given date/timestamp/string.
- dayofyear(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Extracts the day of the year as an integer from a given date/timestamp/string.
- days(String) - 类 中的静态方法org.apache.spark.sql.connector.expressions.Expressions
-
Create a daily transform for a timestamp or date column.
- days(String) - 类 中的静态方法org.apache.spark.sql.connector.expressions.LogicalExpressions
-
- days(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
A transform for timestamps and dates to partition data into days.
- DB2Dialect - org.apache.spark.sql.jdbc中的类
-
- DB2Dialect() - 类 的构造器org.apache.spark.sql.jdbc.DB2Dialect
-
- DCT - org.apache.spark.ml.feature中的类
-
A feature transformer that takes the 1D discrete cosine transform of a real vector.
- DCT(String) - 类 的构造器org.apache.spark.ml.feature.DCT
-
- DCT() - 类 的构造器org.apache.spark.ml.feature.DCT
-
- deallocate() - 类 中的方法org.apache.spark.storage.ReadableChannelFileRegion
-
- decayFactor() - 类 中的方法org.apache.spark.mllib.clustering.StreamingKMeans
-
- decide(LoggingEvent) - 类 中的方法org.apache.spark.internal.SparkShellLoggingFilter
-
If sparkShellThresholdLevel is not defined, this filter is a no-op.
- decimal() - 类 中的方法org.apache.spark.sql.ColumnName
-
Creates a new StructField of type decimal.
- decimal(int, int) - 类 中的方法org.apache.spark.sql.ColumnName
-
Creates a new StructField of type decimal.
- DECIMAL() - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for nullable decimal type.
- Decimal - org.apache.spark.sql.types中的类
-
A mutable implementation of BigDecimal that can hold a Long if values are small enough.
- Decimal() - 类 的构造器org.apache.spark.sql.types.Decimal
-
- Decimal.DecimalAsIfIntegral$ - org.apache.spark.sql.types中的类
-
A Integral evidence parameter for Decimals.
- Decimal.DecimalIsConflicted - org.apache.spark.sql.types中的接口
-
Common methods for Decimal evidence parameters
- Decimal.DecimalIsFractional$ - org.apache.spark.sql.types中的类
-
A Fractional evidence parameter for Decimals.
- DecimalAsIfIntegral$() - 类 的构造器org.apache.spark.sql.types.Decimal.DecimalAsIfIntegral$
-
- DecimalExactNumeric - org.apache.spark.sql.types中的类
-
- DecimalExactNumeric() - 类 的构造器org.apache.spark.sql.types.DecimalExactNumeric
-
- DecimalIsFractional$() - 类 的构造器org.apache.spark.sql.types.Decimal.DecimalIsFractional$
-
- DecimalType - org.apache.spark.sql.types中的类
-
The data type representing java.math.BigDecimal values.
- DecimalType(int, int) - 类 的构造器org.apache.spark.sql.types.DecimalType
-
- DecimalType(int) - 类 的构造器org.apache.spark.sql.types.DecimalType
-
- DecimalType() - 类 的构造器org.apache.spark.sql.types.DecimalType
-
- DecimalType.Expression$ - org.apache.spark.sql.types中的类
-
- DecimalType.Fixed$ - org.apache.spark.sql.types中的类
-
- decimalTypeInfoToCatalyst(PrimitiveObjectInspector) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- DecisionTree - org.apache.spark.mllib.tree中的类
-
A class which implements a decision tree learning algorithm for classification and regression.
- DecisionTree(Strategy) - 类 的构造器org.apache.spark.mllib.tree.DecisionTree
-
- DecisionTreeClassificationModel - org.apache.spark.ml.classification中的类
-
Decision tree model (http://en.wikipedia.org/wiki/Decision_tree_learning) for classification.
- DecisionTreeClassifier - org.apache.spark.ml.classification中的类
-
Decision tree learning algorithm (http://en.wikipedia.org/wiki/Decision_tree_learning)
for classification.
- DecisionTreeClassifier(String) - 类 的构造器org.apache.spark.ml.classification.DecisionTreeClassifier
-
- DecisionTreeClassifier() - 类 的构造器org.apache.spark.ml.classification.DecisionTreeClassifier
-
- DecisionTreeClassifierParams - org.apache.spark.ml.tree中的接口
-
- DecisionTreeModel - org.apache.spark.ml.tree中的接口
-
Abstraction for Decision Tree models.
- DecisionTreeModel - org.apache.spark.mllib.tree.model中的类
-
Decision tree model for classification or regression.
- DecisionTreeModel(Node, Enumeration.Value) - 类 的构造器org.apache.spark.mllib.tree.model.DecisionTreeModel
-
- DecisionTreeModel.SaveLoadV1_0$ - org.apache.spark.mllib.tree.model中的类
-
- DecisionTreeModel.SaveLoadV1_0$.NodeData - org.apache.spark.mllib.tree.model中的类
-
Model data for model import/export
- DecisionTreeModel.SaveLoadV1_0$.NodeData$ - org.apache.spark.mllib.tree.model中的类
-
- DecisionTreeModel.SaveLoadV1_0$.PredictData - org.apache.spark.mllib.tree.model中的类
-
- DecisionTreeModel.SaveLoadV1_0$.PredictData$ - org.apache.spark.mllib.tree.model中的类
-
- DecisionTreeModel.SaveLoadV1_0$.SplitData - org.apache.spark.mllib.tree.model中的类
-
- DecisionTreeModel.SaveLoadV1_0$.SplitData$ - org.apache.spark.mllib.tree.model中的类
-
- DecisionTreeModelReadWrite - org.apache.spark.ml.tree中的类
-
Helper classes for tree model persistence
- DecisionTreeModelReadWrite() - 类 的构造器org.apache.spark.ml.tree.DecisionTreeModelReadWrite
-
- DecisionTreeModelReadWrite.NodeData - org.apache.spark.ml.tree中的类
-
Info for a
Node
param: id Index used for tree reconstruction.
- DecisionTreeModelReadWrite.NodeData$ - org.apache.spark.ml.tree中的类
-
- DecisionTreeModelReadWrite.SplitData - org.apache.spark.ml.tree中的类
-
Info for a
Split
param: featureIndex Index of feature split on
param: leftCategoriesOrThreshold For categorical feature, set of leftCategories.
- DecisionTreeModelReadWrite.SplitData$ - org.apache.spark.ml.tree中的类
-
- DecisionTreeParams - org.apache.spark.ml.tree中的接口
-
Parameters for Decision Tree-based algorithms.
- DecisionTreeRegressionModel - org.apache.spark.ml.regression中的类
-
- DecisionTreeRegressor - org.apache.spark.ml.regression中的类
-
- DecisionTreeRegressor(String) - 类 的构造器org.apache.spark.ml.regression.DecisionTreeRegressor
-
- DecisionTreeRegressor() - 类 的构造器org.apache.spark.ml.regression.DecisionTreeRegressor
-
- DecisionTreeRegressorParams - org.apache.spark.ml.tree中的接口
-
- decode(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the first argument into a string from a binary using the provided character set
(one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
- decodeFileNameInURI(URI) - 类 中的静态方法org.apache.spark.util.Utils
-
Get the file name from uri's raw path and decode it.
- decodeStructField(StructField, boolean) - 接口 中的方法org.apache.spark.ml.attribute.AttributeFactory
-
Creates an
Attribute from a
StructField instance, optionally preserving name.
- decodeURLParameter(String) - 类 中的静态方法org.apache.spark.ui.UIUtils
-
Decode URLParameter if URL is encoded by YARN-WebAppProxyServlet.
- DedicatedMessageLoop - org.apache.spark.rpc.netty中的类
-
A message loop that is dedicated to a single RPC endpoint.
- DedicatedMessageLoop(String, IsolatedRpcEndpoint, Dispatcher) - 类 的构造器org.apache.spark.rpc.netty.DedicatedMessageLoop
-
- DEFAULT_CORES() - 类 中的静态方法org.apache.spark.internal.config.Deploy
-
- DEFAULT_DRIVER_MEM_MB() - 类 中的静态方法org.apache.spark.util.Utils
-
Define a default value for driver memory here since this value is referenced across the code
base and nearly all files already use Utils.scala
- DEFAULT_LOG_DIR() - 类 中的静态方法org.apache.spark.internal.config.History
-
- DEFAULT_MAX_FAILURES() - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- DEFAULT_NUM_OUTPUT_ROWS() - 类 中的静态方法org.apache.spark.sql.streaming.SinkProgress
-
- DEFAULT_NUMBER_EXECUTORS() - 类 中的静态方法org.apache.spark.scheduler.cluster.SchedulerBackendUtils
-
- DEFAULT_ROLLING_INTERVAL_SECS() - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- DEFAULT_SASL_KERBEROS_SERVICE_NAME() - 类 中的静态方法org.apache.spark.kafka010.KafkaTokenSparkConf
-
- DEFAULT_SASL_TOKEN_MECHANISM() - 类 中的静态方法org.apache.spark.kafka010.KafkaTokenSparkConf
-
- DEFAULT_SECURITY_PROTOCOL_CONFIG() - 类 中的静态方法org.apache.spark.kafka010.KafkaTokenSparkConf
-
- DEFAULT_SHUTDOWN_PRIORITY() - 类 中的静态方法org.apache.spark.util.ShutdownHookManager
-
- DEFAULT_TARGET_SERVERS_REGEX() - 类 中的静态方法org.apache.spark.kafka010.KafkaTokenSparkConf
-
- defaultAttr() - 类 中的静态方法org.apache.spark.ml.attribute.BinaryAttribute
-
The default binary attribute.
- defaultAttr() - 类 中的静态方法org.apache.spark.ml.attribute.NominalAttribute
-
The default nominal attribute.
- defaultAttr() - 类 中的静态方法org.apache.spark.ml.attribute.NumericAttribute
-
The default numeric attribute.
- defaultCopy(ParamMap) - 接口 中的方法org.apache.spark.ml.param.Params
-
Default implementation of copy with extra params.
- defaultCorrName() - 类 中的静态方法org.apache.spark.mllib.stat.correlation.CorrelationNames
-
- DefaultCredentials - org.apache.spark.streaming.kinesis中的类
-
Returns DefaultAWSCredentialsProviderChain for authentication.
- DefaultCredentials() - 类 的构造器org.apache.spark.streaming.kinesis.DefaultCredentials
-
- defaultLink() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Binomial$
-
- defaultLink() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Gamma$
-
- defaultLink() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Gaussian$
-
- defaultLink() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Poisson$
-
- defaultMinPartitions() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Default min number of partitions for Hadoop RDDs when not given by user
- defaultMinPartitions() - 类 中的方法org.apache.spark.SparkContext
-
Default min number of partitions for Hadoop RDDs when not given by user
Notice that we use math.min so the "defaultMinPartitions" cannot be higher than 2.
- defaultNamespace() - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- defaultNamespace() - 接口 中的方法org.apache.spark.sql.connector.catalog.SupportsNamespaces
-
Return a default namespace for the catalog.
- defaultParallelism() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Default level of parallelism to use when not given by user (e.g. parallelize and makeRDD).
- defaultParallelism() - 接口 中的方法org.apache.spark.scheduler.SchedulerBackend
-
- defaultParallelism() - 接口 中的方法org.apache.spark.scheduler.TaskScheduler
-
- defaultParallelism() - 类 中的方法org.apache.spark.SparkContext
-
Default level of parallelism to use when not given by user (e.g. parallelize and makeRDD).
- defaultParamMap() - 接口 中的方法org.apache.spark.ml.param.Params
-
Internal param map for default values.
- defaultParams(String) - 类 中的静态方法org.apache.spark.mllib.tree.configuration.BoostingStrategy
-
Returns default configuration for the boosting algorithm
- defaultParams(Enumeration.Value) - 类 中的静态方法org.apache.spark.mllib.tree.configuration.BoostingStrategy
-
Returns default configuration for the boosting algorithm
- DefaultParamsReadable<T> - org.apache.spark.ml.util中的接口
-
:: DeveloperApi ::
Helper trait for making simple Params types readable.
- DefaultParamsWritable - org.apache.spark.ml.util中的接口
-
:: DeveloperApi ::
Helper trait for making simple Params types writable.
- DefaultPartitionCoalescer - org.apache.spark.rdd中的类
-
Coalesce the partitions of a parent RDD (prev) into fewer partitions, so that each partition of
this RDD computes one or more of the parent ones.
- DefaultPartitionCoalescer(double) - 类 的构造器org.apache.spark.rdd.DefaultPartitionCoalescer
-
- DefaultPartitionCoalescer.partitionGroupOrdering$ - org.apache.spark.rdd中的类
-
- defaultPartitioner(RDD<?>, Seq<RDD<?>>) - 类 中的静态方法org.apache.spark.Partitioner
-
Choose a partitioner to use for a cogroup-like operation between a number of RDDs.
- defaultSize() - 类 中的方法org.apache.spark.sql.types.ArrayType
-
The default size of a value of the ArrayType is the default size of the element type.
- defaultSize() - 类 中的方法org.apache.spark.sql.types.BinaryType
-
The default size of a value of the BinaryType is 100 bytes.
- defaultSize() - 类 中的方法org.apache.spark.sql.types.BooleanType
-
The default size of a value of the BooleanType is 1 byte.
- defaultSize() - 类 中的方法org.apache.spark.sql.types.ByteType
-
The default size of a value of the ByteType is 1 byte.
- defaultSize() - 类 中的方法org.apache.spark.sql.types.CalendarIntervalType
-
- defaultSize() - 类 中的方法org.apache.spark.sql.types.DataType
-
The default size of a value of this data type, used internally for size estimation.
- defaultSize() - 类 中的方法org.apache.spark.sql.types.DateType
-
The default size of a value of the DateType is 4 bytes.
- defaultSize() - 类 中的方法org.apache.spark.sql.types.DecimalType
-
The default size of a value of the DecimalType is 8 bytes when precision is at most 18,
and 16 bytes otherwise.
- defaultSize() - 类 中的方法org.apache.spark.sql.types.DoubleType
-
The default size of a value of the DoubleType is 8 bytes.
- defaultSize() - 类 中的方法org.apache.spark.sql.types.FloatType
-
The default size of a value of the FloatType is 4 bytes.
- defaultSize() - 类 中的方法org.apache.spark.sql.types.HiveStringType
-
- defaultSize() - 类 中的方法org.apache.spark.sql.types.IntegerType
-
The default size of a value of the IntegerType is 4 bytes.
- defaultSize() - 类 中的方法org.apache.spark.sql.types.LongType
-
The default size of a value of the LongType is 8 bytes.
- defaultSize() - 类 中的方法org.apache.spark.sql.types.MapType
-
The default size of a value of the MapType is
(the default size of the key type + the default size of the value type).
- defaultSize() - 类 中的方法org.apache.spark.sql.types.NullType
-
- defaultSize() - 类 中的方法org.apache.spark.sql.types.ObjectType
-
- defaultSize() - 类 中的方法org.apache.spark.sql.types.ShortType
-
The default size of a value of the ShortType is 2 bytes.
- defaultSize() - 类 中的方法org.apache.spark.sql.types.StringType
-
The default size of a value of the StringType is 20 bytes.
- defaultSize() - 类 中的方法org.apache.spark.sql.types.StructType
-
The default size of a value of the StructType is the total default sizes of all field types.
- defaultSize() - 类 中的方法org.apache.spark.sql.types.TimestampType
-
The default size of a value of the TimestampType is 8 bytes.
- defaultStrategy(String) - 类 中的静态方法org.apache.spark.mllib.tree.configuration.Strategy
-
- defaultStrategy(Enumeration.Value) - 类 中的静态方法org.apache.spark.mllib.tree.configuration.Strategy
-
- DefaultTopologyMapper - org.apache.spark.storage中的类
-
A TopologyMapper that assumes all nodes are in the same rack
- DefaultTopologyMapper(SparkConf) - 类 的构造器org.apache.spark.storage.DefaultTopologyMapper
-
- defaultValue() - 类 中的方法org.apache.spark.internal.config.ConfigEntryWithDefault
-
- defaultValue() - 类 中的方法org.apache.spark.internal.config.ConfigEntryWithDefaultFunction
-
- defaultValue() - 类 中的方法org.apache.spark.internal.config.ConfigEntryWithDefaultString
-
- defaultValueString() - 类 中的方法org.apache.spark.internal.config.ConfigEntryWithDefault
-
- defaultValueString() - 类 中的方法org.apache.spark.internal.config.ConfigEntryWithDefaultFunction
-
- defaultValueString() - 类 中的方法org.apache.spark.internal.config.ConfigEntryWithDefaultString
-
- degree() - 类 中的方法org.apache.spark.ml.feature.PolynomialExpansion
-
The polynomial degree to expand, which should be greater than equal to 1.
- degrees() - 类 中的方法org.apache.spark.graphx.GraphOps
-
- degrees(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Converts an angle measured in radians to an approximately equivalent angle measured in degrees.
- degrees(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Converts an angle measured in radians to an approximately equivalent angle measured in degrees.
- degreesOfFreedom() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary
-
- degreesOfFreedom() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionSummary
-
Degrees of freedom
- degreesOfFreedom() - 类 中的方法org.apache.spark.mllib.stat.test.ChiSqTestResult
-
- degreesOfFreedom() - 类 中的方法org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult
-
- degreesOfFreedom() - 接口 中的方法org.apache.spark.mllib.stat.test.TestResult
-
Returns the degree(s) of freedom of the hypothesis test.
- delegate() - 类 中的方法org.apache.spark.InterruptibleIterator
-
- DelegatingCatalogExtension - org.apache.spark.sql.connector.catalog中的类
-
A simple implementation of
CatalogExtension, which implements all the catalog functions
by calling the built-in session catalog directly.
- DelegatingCatalogExtension() - 类 的构造器org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- delegationTokensRequired(SparkConf, Configuration) - 接口 中的方法org.apache.spark.security.HadoopDelegationTokenProvider
-
Returns true if delegation tokens are required for this service.
- deleteCheckpointFiles() - 类 中的方法org.apache.spark.ml.clustering.DistributedLDAModel
-
:: DeveloperApi ::
Remove any remaining checkpoint files from training.
- deleteColumn(String[]) - 接口 中的静态方法org.apache.spark.sql.connector.catalog.TableChange
-
Create a TableChange for deleting a field.
- deleteExternalTmpPath(Configuration) - 接口 中的方法org.apache.spark.sql.hive.execution.SaveAsHiveFile
-
- deleteRecursively(File) - 类 中的静态方法org.apache.spark.util.Utils
-
Delete a file or directory and its contents recursively.
- deleteWhere(Filter[]) - 接口 中的方法org.apache.spark.sql.connector.catalog.SupportsDelete
-
Delete data from a data source table that matches filter expressions.
- deleteWithJob(FileSystem, Path, boolean) - 类 中的方法org.apache.spark.internal.io.FileCommitProtocol
-
Specifies that a file should be deleted with the commit of this job.
- delimiterOptions() - 类 中的静态方法org.apache.spark.sql.hive.execution.HiveOptions
-
- delta() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Tweedie$
-
Constant used in initialization and deviance to avoid numerical issues.
- dense(int, int, double[]) - 类 中的静态方法org.apache.spark.ml.linalg.Matrices
-
Creates a column-major dense matrix.
- dense(double, double...) - 类 中的静态方法org.apache.spark.ml.linalg.Vectors
-
Creates a dense vector from its values.
- dense(double, Seq<Object>) - 类 中的静态方法org.apache.spark.ml.linalg.Vectors
-
Creates a dense vector from its values.
- dense(double[]) - 类 中的静态方法org.apache.spark.ml.linalg.Vectors
-
Creates a dense vector from a double array.
- dense(int, int, double[]) - 类 中的静态方法org.apache.spark.mllib.linalg.Matrices
-
Creates a column-major dense matrix.
- dense(double, double...) - 类 中的静态方法org.apache.spark.mllib.linalg.Vectors
-
Creates a dense vector from its values.
- dense(double, Seq<Object>) - 类 中的静态方法org.apache.spark.mllib.linalg.Vectors
-
Creates a dense vector from its values.
- dense(double[]) - 类 中的静态方法org.apache.spark.mllib.linalg.Vectors
-
Creates a dense vector from a double array.
- dense_rank() - 类 中的静态方法org.apache.spark.sql.functions
-
Window function: returns the rank of rows within a window partition, without any gaps.
- DenseMatrix - org.apache.spark.ml.linalg中的类
-
Column-major dense matrix.
- DenseMatrix(int, int, double[], boolean) - 类 的构造器org.apache.spark.ml.linalg.DenseMatrix
-
- DenseMatrix(int, int, double[]) - 类 的构造器org.apache.spark.ml.linalg.DenseMatrix
-
Column-major dense matrix.
- DenseMatrix - org.apache.spark.mllib.linalg中的类
-
Column-major dense matrix.
- DenseMatrix(int, int, double[], boolean) - 类 的构造器org.apache.spark.mllib.linalg.DenseMatrix
-
- DenseMatrix(int, int, double[]) - 类 的构造器org.apache.spark.mllib.linalg.DenseMatrix
-
Column-major dense matrix.
- DenseVector - org.apache.spark.ml.linalg中的类
-
A dense vector represented by a value array.
- DenseVector(double[]) - 类 的构造器org.apache.spark.ml.linalg.DenseVector
-
- DenseVector - org.apache.spark.mllib.linalg中的类
-
A dense vector represented by a value array.
- DenseVector(double[]) - 类 的构造器org.apache.spark.mllib.linalg.DenseVector
-
- dependencies() - 类 中的方法org.apache.spark.rdd.RDD
-
Get the list of dependencies of this RDD, taking into account whether the
RDD is checkpointed or not.
- dependencies() - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
List of parent DStreams on which this DStream depends on
- dependencies() - 类 中的方法org.apache.spark.streaming.dstream.InputDStream
-
- Dependency<T> - org.apache.spark中的类
-
:: DeveloperApi ::
Base class for dependencies.
- Dependency() - 类 的构造器org.apache.spark.Dependency
-
- Deploy - org.apache.spark.internal.config中的类
-
- Deploy() - 类 的构造器org.apache.spark.internal.config.Deploy
-
- DEPLOY_MODE - 类 中的静态变量org.apache.spark.launcher.SparkLauncher
-
The Spark deploy mode.
- deployMode() - 类 中的方法org.apache.spark.SparkContext
-
- depth() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- depth() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- depth() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeModel
-
Depth of the tree.
- depth() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel
-
Get depth of tree.
- depth() - 类 中的方法org.apache.spark.util.sketch.CountMinSketch
-
- DerbyDialect - org.apache.spark.sql.jdbc中的类
-
- DerbyDialect() - 类 的构造器org.apache.spark.sql.jdbc.DerbyDialect
-
- deriv(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.CLogLog$
-
- deriv(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Identity$
-
- deriv(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Inverse$
-
- deriv(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Log$
-
- deriv(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Logit$
-
- deriv(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Probit$
-
- deriv(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Sqrt$
-
- derivative() - 接口 中的方法org.apache.spark.ml.ann.ActivationFunction
-
Implements a derivative of a function (needed for the back propagation)
- desc() - 类 中的方法org.apache.spark.serializer.SerializationDebugger.ObjectStreamClassMethods
-
- desc() - 类 中的方法org.apache.spark.sql.Column
-
Returns a sort expression based on the descending order of the column.
- desc(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns a sort expression based on the descending order of the column.
- desc() - 类 中的方法org.apache.spark.util.MethodIdentifier
-
- desc_nulls_first() - 类 中的方法org.apache.spark.sql.Column
-
Returns a sort expression based on the descending order of the column,
and null values appear before non-null values.
- desc_nulls_first(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns a sort expression based on the descending order of the column,
and null values appear before non-null values.
- desc_nulls_last() - 类 中的方法org.apache.spark.sql.Column
-
Returns a sort expression based on the descending order of the column,
and null values appear after non-null values.
- desc_nulls_last(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns a sort expression based on the descending order of the column,
and null values appear after non-null values.
- describe() - 接口 中的方法org.apache.spark.sql.connector.expressions.Expression
-
Format the expression as a human readable SQL-like string.
- describe(String...) - 类 中的方法org.apache.spark.sql.Dataset
-
Computes basic statistics for numeric and string columns, including count, mean, stddev, min,
and max.
- describe(Seq<String>) - 类 中的方法org.apache.spark.sql.Dataset
-
Computes basic statistics for numeric and string columns, including count, mean, stddev, min,
and max.
- describeTopics(int) - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
Return the topics described by their top-weighted terms.
- describeTopics() - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
- describeTopics(int) - 类 中的方法org.apache.spark.mllib.clustering.DistributedLDAModel
-
- describeTopics(int) - 类 中的方法org.apache.spark.mllib.clustering.LDAModel
-
Return the topics described by weighted terms.
- describeTopics() - 类 中的方法org.apache.spark.mllib.clustering.LDAModel
-
Return the topics described by weighted terms.
- describeTopics(int) - 类 中的方法org.apache.spark.mllib.clustering.LocalLDAModel
-
- description() - 类 中的方法org.apache.spark.ExceptionFailure
-
- description() - 类 中的方法org.apache.spark.sql.catalog.Column
-
- description() - 类 中的方法org.apache.spark.sql.catalog.Database
-
- description() - 类 中的方法org.apache.spark.sql.catalog.Function
-
- description() - 类 中的方法org.apache.spark.sql.catalog.Table
-
- description() - 接口 中的方法org.apache.spark.sql.connector.read.Scan
-
A description string of this scan, which may includes information like: what filters are
configured for this scan, what's the value of some important options like path, etc.
- description() - 类 中的方法org.apache.spark.sql.streaming.SinkProgress
-
- description() - 类 中的方法org.apache.spark.sql.streaming.SourceProgress
-
- description() - 类 中的方法org.apache.spark.status.api.v1.JobData
-
- description() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- description() - 类 中的方法org.apache.spark.status.api.v1.streaming.OutputOperationInfo
-
- description() - 类 中的方法org.apache.spark.status.LiveStage
-
- description() - 类 中的方法org.apache.spark.storage.StorageLevel
-
- description() - 类 中的方法org.apache.spark.streaming.scheduler.OutputOperationInfo
-
- DESER_CPU_TIME() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- DESER_TIME() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- DeserializationStream - org.apache.spark.serializer中的类
-
:: DeveloperApi ::
A stream for reading serialized objects.
- DeserializationStream() - 类 的构造器org.apache.spark.serializer.DeserializationStream
-
- deserialize(Object) - 类 中的方法org.apache.spark.mllib.linalg.VectorUDT
-
- deserialize(ByteBuffer, ClassLoader, ClassTag<T>) - 类 中的方法org.apache.spark.serializer.DummySerializerInstance
-
- deserialize(ByteBuffer, ClassTag<T>) - 类 中的方法org.apache.spark.serializer.DummySerializerInstance
-
- deserialize(ByteBuffer, ClassTag<T>) - 类 中的方法org.apache.spark.serializer.SerializerInstance
-
- deserialize(ByteBuffer, ClassLoader, ClassTag<T>) - 类 中的方法org.apache.spark.serializer.SerializerInstance
-
- deserialize(byte[]) - 类 中的静态方法org.apache.spark.util.Utils
-
Deserialize an object using Java serialization
- deserialize(byte[], ClassLoader) - 类 中的静态方法org.apache.spark.util.Utils
-
Deserialize an object using Java serialization and the given ClassLoader
- deserialized() - 类 中的方法org.apache.spark.storage.StorageLevel
-
- DeserializedMemoryEntry<T> - org.apache.spark.storage.memory中的类
-
- DeserializedMemoryEntry(Object, long, ClassTag<T>) - 类 的构造器org.apache.spark.storage.memory.DeserializedMemoryEntry
-
- DeserializedValuesHolder<T> - org.apache.spark.storage.memory中的类
-
A holder for storing the deserialized values.
- DeserializedValuesHolder(ClassTag<T>) - 类 的构造器org.apache.spark.storage.memory.DeserializedValuesHolder
-
- deserializeLongValue(byte[]) - 类 中的静态方法org.apache.spark.util.Utils
-
Deserialize a Long value (used for PythonPartitioner)
- deserializeOffset(String) - 接口 中的方法org.apache.spark.sql.connector.read.streaming.SparkDataStream
-
Deserialize a JSON string into an Offset of the implementation-defined offset type.
- deserializeStream(InputStream) - 类 中的方法org.apache.spark.serializer.DummySerializerInstance
-
- deserializeStream(InputStream) - 类 中的方法org.apache.spark.serializer.SerializerInstance
-
- deserializeViaNestedStream(InputStream, SerializerInstance, Function1<DeserializationStream, BoxedUnit>) - 类 中的静态方法org.apache.spark.util.Utils
-
Deserialize via nested stream using specific serializer
- destroy() - 类 中的方法org.apache.spark.broadcast.Broadcast
-
Destroy all data and metadata related to this broadcast variable.
- destroy() - 类 中的方法org.apache.spark.ui.HttpSecurityFilter
-
- details() - 类 中的方法org.apache.spark.scheduler.StageInfo
-
- details() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- DETERMINATE() - 类 中的静态方法org.apache.spark.rdd.DeterministicLevel
-
- determineBounds(ArrayBuffer<Tuple2<K, Object>>, int, Ordering<K>, ClassTag<K>) - 类 中的静态方法org.apache.spark.RangePartitioner
-
Determines the bounds for range partitioning from candidates with weights indicating how many
items each represents.
- DetermineTableStats - org.apache.spark.sql.hive中的类
-
- DetermineTableStats(SparkSession) - 类 的构造器org.apache.spark.sql.hive.DetermineTableStats
-
- deterministic() - 类 中的方法org.apache.spark.sql.expressions.UserDefinedAggregateFunction
-
Returns true iff this function is deterministic, i.e. given the same input,
always return the same output.
- deterministic() - 类 中的方法org.apache.spark.sql.expressions.UserDefinedFunction
-
Returns true iff the UDF is deterministic, i.e. the UDF produces the same output given the same
input.
- DeterministicLevel - org.apache.spark.rdd中的类
-
The deterministic level of RDD's output (i.e. what RDD#compute returns).
- DeterministicLevel() - 类 的构造器org.apache.spark.rdd.DeterministicLevel
-
- deviance(double, double, double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Binomial$
-
- deviance(double, double, double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Gamma$
-
- deviance(double, double, double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Gaussian$
-
- deviance(double, double, double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Poisson$
-
- deviance() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary
-
- devianceResiduals() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionSummary
-
- dfToCols(Dataset<Row>) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
- dfToRowRDD(Dataset<Row>) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
- dgemm(double, DenseMatrix<Object>, DenseMatrix<Object>, double, DenseMatrix<Object>) - 类 中的静态方法org.apache.spark.ml.ann.BreezeUtil
-
DGEMM: C := alpha * A * B + beta * C
- dgemv(double, DenseMatrix<Object>, DenseVector<Object>, double, DenseVector<Object>) - 类 中的静态方法org.apache.spark.ml.ann.BreezeUtil
-
DGEMV: y := alpha * A * x + beta * y
- diag(Vector) - 类 中的静态方法org.apache.spark.ml.linalg.DenseMatrix
-
Generate a diagonal matrix in DenseMatrix format from the supplied values.
- diag(Vector) - 类 中的静态方法org.apache.spark.ml.linalg.Matrices
-
Generate a diagonal matrix in Matrix format from the supplied values.
- diag(Vector) - 类 中的静态方法org.apache.spark.mllib.linalg.DenseMatrix
-
Generate a diagonal matrix in DenseMatrix format from the supplied values.
- diag(Vector) - 类 中的静态方法org.apache.spark.mllib.linalg.Matrices
-
Generate a diagonal matrix in Matrix format from the supplied values.
- diff(RDD<Tuple2<Object, VD>>) - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- diff(VertexRDD<VD>) - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- diff(RDD<Tuple2<Object, VD>>) - 类 中的方法org.apache.spark.graphx.VertexRDD
-
For each vertex present in both this and other, diff returns only those vertices with
differing values; for values that are different, keeps the values from other.
- diff(VertexRDD<VD>) - 类 中的方法org.apache.spark.graphx.VertexRDD
-
For each vertex present in both this and other, diff returns only those vertices with
differing values; for values that are different, keeps the values from other.
- DifferentiableLossAggregator<Datum,Agg extends DifferentiableLossAggregator<Datum,Agg>> - org.apache.spark.ml.optim.aggregator中的接口
-
A parent trait for aggregators used in fitting MLlib models.
- DifferentiableRegularization<T> - org.apache.spark.ml.optim.loss中的接口
-
A Breeze diff function which represents a cost function for differentiable regularization
of parameters. e.g.
- dim() - 接口 中的方法org.apache.spark.ml.optim.aggregator.DifferentiableLossAggregator
-
The dimension of the gradient array.
- dir() - 类 中的方法org.apache.spark.mllib.optimization.NNLS.Workspace
-
- directory(File) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
Sets the working directory of spark-submit.
- DirectPoolMemory - org.apache.spark.metrics中的类
-
- DirectPoolMemory() - 类 的构造器org.apache.spark.metrics.DirectPoolMemory
-
- disableOutputSpecValidation() - 类 中的静态方法org.apache.spark.internal.io.SparkHadoopWriterUtils
-
Allows for the spark.hadoop.validateOutputSpecs checks to be disabled on a case-by-case
basis; see SPARK-4835 for more details.
- disconnect() - 接口 中的方法org.apache.spark.launcher.SparkAppHandle
-
Disconnects the handle from the application, without stopping it.
- DISCOVERY_SCRIPT() - 类 中的静态方法org.apache.spark.resource.ResourceUtils
-
- DISK_BYTES_SPILLED() - 类 中的静态方法org.apache.spark.InternalAccumulator
-
- DISK_ONLY - 类 中的静态变量org.apache.spark.api.java.StorageLevels
-
- DISK_ONLY() - 类 中的静态方法org.apache.spark.storage.StorageLevel
-
- DISK_ONLY_2 - 类 中的静态变量org.apache.spark.api.java.StorageLevels
-
- DISK_ONLY_2() - 类 中的静态方法org.apache.spark.storage.StorageLevel
-
- DISK_SPILL() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- DiskBlockData - org.apache.spark.storage中的类
-
- DiskBlockData(long, long, File, long) - 类 的构造器org.apache.spark.storage.DiskBlockData
-
- diskBytesSpilled() - 类 中的方法org.apache.spark.status.api.v1.ExecutorStageSummary
-
- diskBytesSpilled() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- diskBytesSpilled() - 类 中的方法org.apache.spark.status.api.v1.TaskMetricDistributions
-
- diskBytesSpilled() - 类 中的方法org.apache.spark.status.api.v1.TaskMetrics
-
- diskSize() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.UpdateBlockInfo
-
- diskSize() - 类 中的方法org.apache.spark.storage.BlockStatus
-
- diskSize() - 类 中的方法org.apache.spark.storage.BlockUpdatedInfo
-
- diskSize() - 类 中的方法org.apache.spark.storage.RDDInfo
-
- diskUsed() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- diskUsed() - 类 中的方法org.apache.spark.status.api.v1.RDDDataDistribution
-
- diskUsed() - 类 中的方法org.apache.spark.status.api.v1.RDDPartitionInfo
-
- diskUsed() - 类 中的方法org.apache.spark.status.api.v1.RDDStorageInfo
-
- diskUsed() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- diskUsed() - 类 中的方法org.apache.spark.status.LiveRDD
-
- diskUsed() - 类 中的方法org.apache.spark.status.LiveRDDDistribution
-
- diskUsed() - 类 中的方法org.apache.spark.status.LiveRDDPartition
-
- dispersion() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary
-
- dispose() - 接口 中的方法org.apache.spark.storage.BlockData
-
- dispose() - 类 中的方法org.apache.spark.storage.DiskBlockData
-
- dispose(ByteBuffer) - 类 中的静态方法org.apache.spark.storage.StorageUtils
-
Attempt to clean up a ByteBuffer if it is direct or memory-mapped.
- distanceMeasure() - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeans
-
- distanceMeasure() - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
- distanceMeasure() - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- distanceMeasure() - 类 中的方法org.apache.spark.ml.clustering.KMeansModel
-
- distanceMeasure() - 类 中的方法org.apache.spark.ml.evaluation.ClusteringEvaluator
-
param for distance measure to be used in evaluation
(supports "squaredEuclidean" (default), "cosine")
- distanceMeasure() - 接口 中的方法org.apache.spark.ml.param.shared.HasDistanceMeasure
-
Param for The distance measure.
- distanceMeasure() - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeansModel
-
- distanceMeasure() - 类 中的方法org.apache.spark.mllib.clustering.KMeansModel
-
- distinct() - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Return a new RDD containing the distinct elements in this RDD.
- distinct(int) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Return a new RDD containing the distinct elements in this RDD.
- distinct() - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return a new RDD containing the distinct elements in this RDD.
- distinct(int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return a new RDD containing the distinct elements in this RDD.
- distinct() - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Return a new RDD containing the distinct elements in this RDD.
- distinct(int) - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Return a new RDD containing the distinct elements in this RDD.
- distinct(int, Ordering<T>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return a new RDD containing the distinct elements in this RDD.
- distinct() - 类 中的方法org.apache.spark.rdd.RDD
-
Return a new RDD containing the distinct elements in this RDD.
- distinct() - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset that contains only the unique rows from this Dataset.
- distinct(Column...) - 类 中的方法org.apache.spark.sql.expressions.UserDefinedAggregateFunction
-
Creates a Column for this UDAF using the distinct values of the given
Columns as input arguments.
- distinct(Seq<Column>) - 类 中的方法org.apache.spark.sql.expressions.UserDefinedAggregateFunction
-
Creates a Column for this UDAF using the distinct values of the given
Columns as input arguments.
- DistributedLDAModel - org.apache.spark.ml.clustering中的类
-
Distributed model fitted by
LDA.
- DistributedLDAModel - org.apache.spark.mllib.clustering中的类
-
Distributed LDA model.
- DistributedMatrix - org.apache.spark.mllib.linalg.distributed中的接口
-
Represents a distributively stored matrix backed by one or more RDDs.
- Distribution - org.apache.spark.sql.connector.read.partitioning中的接口
-
An interface to represent data distribution requirement, which specifies how the records should
be distributed among the data partitions (one
PartitionReader outputs data for one
partition).
- distribution(LiveExecutor) - 类 中的方法org.apache.spark.status.LiveRDD
-
- distributionOpt(LiveExecutor) - 类 中的方法org.apache.spark.status.LiveRDD
-
- div(Decimal, Decimal) - 类 中的方法org.apache.spark.sql.types.Decimal.DecimalIsFractional$
-
- div(double, double) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- div(float, float) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- div(Duration) - 类 中的方法org.apache.spark.streaming.Duration
-
- divide(Object) - 类 中的方法org.apache.spark.sql.Column
-
Division this expression by another expression.
- doc() - 类 中的方法org.apache.spark.ml.param.Param
-
- docConcentration() - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- docConcentration() - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
- docConcentration() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
Concentration parameter (commonly named "alpha") for the prior placed on documents'
distributions over topics ("theta").
- docConcentration() - 类 中的方法org.apache.spark.mllib.clustering.DistributedLDAModel
-
- docConcentration() - 类 中的方法org.apache.spark.mllib.clustering.LDAModel
-
Concentration parameter (commonly named "alpha") for the prior placed on documents'
distributions over topics ("theta").
- docConcentration() - 类 中的方法org.apache.spark.mllib.clustering.LocalLDAModel
-
- docFreq() - 类 中的方法org.apache.spark.ml.feature.IDFModel
-
Returns the document frequency
- docFreq() - 类 中的方法org.apache.spark.mllib.feature.IDFModel
-
- DocumentFrequencyAggregator(int) - 类 的构造器org.apache.spark.mllib.feature.IDF.DocumentFrequencyAggregator
-
- DocumentFrequencyAggregator() - 类 的构造器org.apache.spark.mllib.feature.IDF.DocumentFrequencyAggregator
-
- doesDirectoryContainAnyNewFiles(File, long) - 类 中的静态方法org.apache.spark.util.Utils
-
Determines if a directory contains any files newer than cutoff seconds.
- doFetchFile(String, File, String, SparkConf, org.apache.spark.SecurityManager, Configuration) - 类 中的静态方法org.apache.spark.util.Utils
-
Download a file or directory to target directory.
- doFilter(ServletRequest, ServletResponse, FilterChain) - 类 中的方法org.apache.spark.ui.HttpSecurityFilter
-
- doPostEvent(SparkListenerInterface, SparkListenerEvent) - 接口 中的方法org.apache.spark.scheduler.SparkListenerBus
-
- doPostEvent(L, E) - 接口 中的方法org.apache.spark.util.ListenerBus
-
Post an event to the specified listener.
- Dot - org.apache.spark.ml.feature中的类
-
- Dot() - 类 的构造器org.apache.spark.ml.feature.Dot
-
- dot(Vector, Vector) - 类 中的静态方法org.apache.spark.ml.linalg.BLAS
-
dot(x, y)
- dot(Vector) - 接口 中的方法org.apache.spark.ml.linalg.Vector
-
Calculate the dot product of this vector with another.
- dot(Vector, Vector) - 类 中的静态方法org.apache.spark.mllib.linalg.BLAS
-
dot(x, y)
- dot(Vector) - 接口 中的方法org.apache.spark.mllib.linalg.Vector
-
Calculate the dot product of this vector with another.
- doTest(DStream<Tuple2<StatCounter, StatCounter>>) - 接口 中的方法org.apache.spark.mllib.stat.test.StreamingTestMethod
-
Perform streaming 2-sample statistical significance testing.
- doTest(DStream<Tuple2<StatCounter, StatCounter>>) - 类 中的静态方法org.apache.spark.mllib.stat.test.StudentTTest
-
- doTest(DStream<Tuple2<StatCounter, StatCounter>>) - 类 中的静态方法org.apache.spark.mllib.stat.test.WelchTTest
-
- DOUBLE() - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for nullable double type.
- doubleAccumulator() - 类 中的方法org.apache.spark.SparkContext
-
Create and register a double accumulator, which starts with 0 and accumulates inputs by add.
- doubleAccumulator(String) - 类 中的方法org.apache.spark.SparkContext
-
Create and register a double accumulator, which starts with 0 and accumulates inputs by add.
- DoubleAccumulator - org.apache.spark.util中的类
-
An
accumulator for computing sum, count, and averages for double precision
floating numbers.
- DoubleAccumulator() - 类 的构造器org.apache.spark.util.DoubleAccumulator
-
- DoubleAccumulatorSource - org.apache.spark.metrics.source中的类
-
- DoubleAccumulatorSource() - 类 的构造器org.apache.spark.metrics.source.DoubleAccumulatorSource
-
- DoubleArrayArrayParam - org.apache.spark.ml.param中的类
-
:: DeveloperApi ::
Specialized version of Param[Array[Array[Double}] for Java.
- DoubleArrayArrayParam(Params, String, String, Function1<double[][], Object>) - 类 的构造器org.apache.spark.ml.param.DoubleArrayArrayParam
-
- DoubleArrayArrayParam(Params, String, String) - 类 的构造器org.apache.spark.ml.param.DoubleArrayArrayParam
-
- DoubleArrayParam - org.apache.spark.ml.param中的类
-
:: DeveloperApi ::
Specialized version of Param[Array[Double} for Java.
- DoubleArrayParam(Params, String, String, Function1<double[], Object>) - 类 的构造器org.apache.spark.ml.param.DoubleArrayParam
-
- DoubleArrayParam(Params, String, String) - 类 的构造器org.apache.spark.ml.param.DoubleArrayParam
-
- DoubleExactNumeric - org.apache.spark.sql.types中的类
-
- DoubleExactNumeric() - 类 的构造器org.apache.spark.sql.types.DoubleExactNumeric
-
- DoubleFlatMapFunction<T> - org.apache.spark.api.java.function中的接口
-
A function that returns zero or more records of type Double from each input record.
- DoubleFunction<T> - org.apache.spark.api.java.function中的接口
-
A function that returns Doubles, and can be used to construct DoubleRDDs.
- DoubleParam - org.apache.spark.ml.param中的类
-
:: DeveloperApi ::
Specialized version of Param[Double] for Java.
- DoubleParam(String, String, String, Function1<Object, Object>) - 类 的构造器org.apache.spark.ml.param.DoubleParam
-
- DoubleParam(String, String, String) - 类 的构造器org.apache.spark.ml.param.DoubleParam
-
- DoubleParam(Identifiable, String, String, Function1<Object, Object>) - 类 的构造器org.apache.spark.ml.param.DoubleParam
-
- DoubleParam(Identifiable, String, String) - 类 的构造器org.apache.spark.ml.param.DoubleParam
-
- DoubleRDDFunctions - org.apache.spark.rdd中的类
-
Extra functions available on RDDs of Doubles through an implicit conversion.
- DoubleRDDFunctions(RDD<Object>) - 类 的构造器org.apache.spark.rdd.DoubleRDDFunctions
-
- doubleRDDToDoubleRDDFunctions(RDD<Object>) - 类 中的静态方法org.apache.spark.rdd.RDD
-
- DoubleType - 类 中的静态变量org.apache.spark.sql.types.DataTypes
-
Gets the DoubleType object.
- DoubleType - org.apache.spark.sql.types中的类
-
The data type representing Double values.
- DoubleType() - 类 的构造器org.apache.spark.sql.types.DoubleType
-
- DRIVER() - 类 中的静态方法org.apache.spark.metrics.MetricsSystemInstances
-
- driver() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.SetupDriver
-
- driver() - 接口 中的方法org.apache.spark.shuffle.api.ShuffleDataIO
-
Called once on driver process to bootstrap the shuffle metadata modules that
are maintained by the driver.
- DRIVER_DEFAULT_JAVA_OPTIONS - 类 中的静态变量org.apache.spark.launcher.SparkLauncher
-
Configuration key for the default driver VM options.
- DRIVER_EXTRA_CLASSPATH - 类 中的静态变量org.apache.spark.launcher.SparkLauncher
-
Configuration key for the driver class path.
- DRIVER_EXTRA_JAVA_OPTIONS - 类 中的静态变量org.apache.spark.launcher.SparkLauncher
-
Configuration key for the driver VM options.
- DRIVER_EXTRA_LIBRARY_PATH - 类 中的静态变量org.apache.spark.launcher.SparkLauncher
-
Configuration key for the driver native library path.
- DRIVER_LOG_CLEANER_ENABLED() - 类 中的静态方法org.apache.spark.internal.config.History
-
- DRIVER_LOG_CLEANER_INTERVAL() - 类 中的静态方法org.apache.spark.internal.config.History
-
- DRIVER_MEMORY - 类 中的静态变量org.apache.spark.launcher.SparkLauncher
-
Configuration key for the driver memory.
- DRIVER_WAL_BATCHING_CONF_KEY() - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- DRIVER_WAL_BATCHING_TIMEOUT_CONF_KEY() - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- DRIVER_WAL_CLASS_CONF_KEY() - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- DRIVER_WAL_CLOSE_AFTER_WRITE_CONF_KEY() - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- DRIVER_WAL_MAX_FAILURES_CONF_KEY() - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- DRIVER_WAL_ROLLING_INTERVAL_CONF_KEY() - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- driverAttributes() - 类 中的方法org.apache.spark.scheduler.SparkListenerApplicationStart
-
- driverLogs() - 类 中的方法org.apache.spark.scheduler.SparkListenerApplicationStart
-
- drop() - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Returns a new DataFrame that drops rows containing any null or NaN values.
- drop(String) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Returns a new DataFrame that drops rows containing null or NaN values.
- drop(String[]) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Returns a new DataFrame that drops rows containing any null or NaN values
in the specified columns.
- drop(Seq<String>) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
(Scala-specific) Returns a new DataFrame that drops rows containing any null or NaN values
in the specified columns.
- drop(String, String[]) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Returns a new DataFrame that drops rows containing null or NaN values
in the specified columns.
- drop(String, Seq<String>) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
(Scala-specific) Returns a new DataFrame that drops rows containing null or NaN values
in the specified columns.
- drop(int) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Returns a new DataFrame that drops rows containing
less than minNonNulls non-null and non-NaN values.
- drop(int, String[]) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Returns a new DataFrame that drops rows containing
less than minNonNulls non-null and non-NaN values in the specified columns.
- drop(int, Seq<String>) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
(Scala-specific) Returns a new DataFrame that drops rows containing less than
minNonNulls non-null and non-NaN values in the specified columns.
- drop(String...) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset with columns dropped.
- drop(String) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset with a column dropped.
- drop(Seq<String>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset with columns dropped.
- drop(Column) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset with a column dropped.
- dropDatabase(String, boolean, boolean) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Drop the specified database, if it exists.
- dropDuplicates(String, String...) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new
Dataset with duplicate rows removed, considering only
the subset of columns.
- dropDuplicates() - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset that contains only the unique rows from this Dataset.
- dropDuplicates(Seq<String>) - 类 中的方法org.apache.spark.sql.Dataset
-
(Scala-specific) Returns a new Dataset with duplicate rows removed, considering only
the subset of columns.
- dropDuplicates(String[]) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset with duplicate rows removed, considering only
the subset of columns.
- dropDuplicates(String, Seq<String>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new
Dataset with duplicate rows removed, considering only
the subset of columns.
- dropFromMemory(BlockId, Function0<Either<Object, org.apache.spark.util.io.ChunkedByteBuffer>>, ClassTag<T>) - 接口 中的方法org.apache.spark.storage.memory.BlockEvictionHandler
-
Drop a block from memory, possibly putting it on disk if applicable.
- dropFunction(String, String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Drop an existing function in the database.
- dropGlobalTempView(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Drops the global temporary view with the given view name in the catalog.
- dropLast() - 类 中的方法org.apache.spark.ml.feature.OneHotEncoder
-
- dropLast() - 接口 中的方法org.apache.spark.ml.feature.OneHotEncoderBase
-
Whether to drop the last category in the encoded vector (default: true)
- dropLast() - 类 中的方法org.apache.spark.ml.feature.OneHotEncoderModel
-
- dropNamespace(String[]) - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- dropNamespace(String[]) - 接口 中的方法org.apache.spark.sql.connector.catalog.SupportsNamespaces
-
Drop a namespace from the catalog.
- dropPartitions(String, String, Seq<Map<String, String>>, boolean, boolean, boolean) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Drop one or many partitions in the given table, assuming they exist.
- dropTable(Identifier) - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- dropTable(Identifier) - 接口 中的方法org.apache.spark.sql.connector.catalog.TableCatalog
-
Drop a table in the catalog.
- dropTable(String, String, boolean, boolean) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Drop the specified table.
- dropTempTable(String) - 类 中的方法org.apache.spark.sql.SQLContext
-
Drops the temporary table with the given table name in the catalog.
- dropTempView(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Drops the local temporary view with the given view name in the catalog.
- dspmv(int, double, DenseVector, DenseVector, double, DenseVector) - 类 中的静态方法org.apache.spark.ml.linalg.BLAS
-
y := alpha*A*x + beta*y
- Dst - 类 中的静态变量org.apache.spark.graphx.TripletFields
-
Expose the destination and edge fields but not the source field.
- dstAttr() - 类 中的方法org.apache.spark.graphx.EdgeContext
-
The vertex attribute of the edge's destination vertex.
- dstAttr() - 类 中的方法org.apache.spark.graphx.EdgeTriplet
-
The destination vertex attribute
- dstAttr() - 类 中的方法org.apache.spark.graphx.impl.AggregatingEdgeContext
-
- dstCol() - 类 中的方法org.apache.spark.ml.clustering.PowerIterationClustering
-
- dstCol() - 接口 中的方法org.apache.spark.ml.clustering.PowerIterationClusteringParams
-
Name of the input column for destination vertex IDs.
- dstId() - 类 中的方法org.apache.spark.graphx.Edge
-
- dstId() - 类 中的方法org.apache.spark.graphx.EdgeContext
-
The vertex id of the edge's destination vertex.
- dstId() - 类 中的方法org.apache.spark.graphx.impl.AggregatingEdgeContext
-
- dstream() - 类 中的方法org.apache.spark.streaming.api.java.JavaDStream
-
- dstream() - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
- dstream() - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
- DStream<T> - org.apache.spark.streaming.dstream中的类
-
A Discretized Stream (DStream), the basic abstraction in Spark Streaming, is a continuous
sequence of RDDs (of the same type) representing a continuous stream of data (see
org.apache.spark.rdd.RDD in the Spark core documentation for more details on RDDs).
- DStream(StreamingContext, ClassTag<T>) - 类 的构造器org.apache.spark.streaming.dstream.DStream
-
- dtypes() - 类 中的方法org.apache.spark.sql.Dataset
-
Returns all column names and their data types as an array.
- DummySerializerInstance - org.apache.spark.serializer中的类
-
Unfortunately, we need a serializer instance in order to construct a DiskBlockObjectWriter.
- duration() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
- duration() - 类 中的方法org.apache.spark.status.api.v1.ApplicationAttemptInfo
-
- duration() - 类 中的方法org.apache.spark.status.api.v1.streaming.OutputOperationInfo
-
- duration() - 类 中的方法org.apache.spark.status.api.v1.TaskData
-
- DURATION() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- Duration - org.apache.spark.streaming中的类
-
- Duration(long) - 类 的构造器org.apache.spark.streaming.Duration
-
- duration() - 类 中的方法org.apache.spark.streaming.scheduler.OutputOperationInfo
-
Return the duration of this output operation.
- durationMs() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryProgress
-
- Durations - org.apache.spark.streaming中的类
-
- Durations() - 类 的构造器org.apache.spark.streaming.Durations
-
- f1Measure() - 类 中的方法org.apache.spark.mllib.evaluation.MultilabelMetrics
-
Returns document-based f1-measure averaged by the number of documents
- f1Measure(double) - 类 中的方法org.apache.spark.mllib.evaluation.MultilabelMetrics
-
Returns f1-measure for a given label (category)
- factorial(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the factorial of the given value.
- failed() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
- FAILED() - 类 中的静态方法org.apache.spark.TaskState
-
- failedStages() - 类 中的方法org.apache.spark.status.LiveJob
-
- failedTasks() - 类 中的方法org.apache.spark.status.api.v1.ExecutorStageSummary
-
- failedTasks() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- failedTasks() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- failedTasks() - 类 中的方法org.apache.spark.status.LiveExecutorStageSummary
-
- failedTasks() - 类 中的方法org.apache.spark.status.LiveJob
-
- failedTasks() - 类 中的方法org.apache.spark.status.LiveStage
-
- Failure() - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- failure(String) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- failureReason() - 类 中的方法org.apache.spark.scheduler.StageInfo
-
If the stage failed, the reason why.
- failureReason() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- failureReason() - 类 中的方法org.apache.spark.status.api.v1.streaming.OutputOperationInfo
-
- failureReason() - 类 中的方法org.apache.spark.streaming.scheduler.OutputOperationInfo
-
- failureReasonCell(String, int, boolean) - 类 中的静态方法org.apache.spark.streaming.ui.UIUtils
-
- FAIR() - 类 中的静态方法org.apache.spark.scheduler.SchedulingMode
-
- FAKE_HIVE_VERSION() - 类 中的静态方法org.apache.spark.sql.hive.HiveUtils
-
- FalsePositiveRate - org.apache.spark.mllib.evaluation.binary中的类
-
False positive rate.
- FalsePositiveRate() - 类 的构造器org.apache.spark.mllib.evaluation.binary.FalsePositiveRate
-
- falsePositiveRate(double) - 类 中的方法org.apache.spark.mllib.evaluation.MulticlassMetrics
-
Returns false positive rate for a given label (category)
- falsePositiveRateByLabel() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionSummary
-
Returns false positive rate for each label (category).
- family() - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
- family() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- family() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionParams
-
Param for the name of family which is a description of the label distribution
to be used in the model.
- family() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
- family() - 接口 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionBase
-
Param for the name of family which is a description of the error distribution
to be used in the model.
- family() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
- Family$() - 类 的构造器org.apache.spark.ml.regression.GeneralizedLinearRegression.Family$
-
- FamilyAndLink$() - 类 的构造器org.apache.spark.ml.regression.GeneralizedLinearRegression.FamilyAndLink$
-
- FAST_IN_PROGRESS_PARSING() - 类 中的静态方法org.apache.spark.internal.config.History
-
- fdr() - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- fdr() - 类 中的方法org.apache.spark.ml.feature.ChiSqSelectorModel
-
- fdr() - 接口 中的方法org.apache.spark.ml.feature.ChiSqSelectorParams
-
The upper bound of the expected false discovery rate.
- fdr() - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelector
-
- feature() - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelectorModel.SaveLoadV1_0$.Data
-
- feature() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.SplitData
-
- feature() - 类 中的方法org.apache.spark.mllib.tree.model.Split
-
- FeatureHasher - org.apache.spark.ml.feature中的类
-
Feature hashing projects a set of categorical or numerical features into a feature vector of
specified dimension (typically substantially smaller than that of the original feature
space).
- FeatureHasher(String) - 类 的构造器org.apache.spark.ml.feature.FeatureHasher
-
- FeatureHasher() - 类 的构造器org.apache.spark.ml.feature.FeatureHasher
-
- featureImportances() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- featureImportances() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- featureImportances() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- featureImportances() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- featureImportances() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- featureImportances() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- featureIndex() - 类 中的方法org.apache.spark.ml.regression.IsotonicRegression
-
- featureIndex() - 接口 中的方法org.apache.spark.ml.regression.IsotonicRegressionBase
-
Param for the index of the feature if featuresCol is a vector column (default: 0), no
effect otherwise.
- featureIndex() - 类 中的方法org.apache.spark.ml.regression.IsotonicRegressionModel
-
- featureIndex() - 类 中的方法org.apache.spark.ml.tree.CategoricalSplit
-
- featureIndex() - 类 中的方法org.apache.spark.ml.tree.ContinuousSplit
-
- featureIndex() - 类 中的方法org.apache.spark.ml.tree.DecisionTreeModelReadWrite.SplitData
-
- featureIndex() - 接口 中的方法org.apache.spark.ml.tree.Split
-
Index of feature which this split tests
- features() - 类 中的方法org.apache.spark.ml.feature.LabeledPoint
-
- features() - 类 中的方法org.apache.spark.mllib.regression.LabeledPoint
-
- featuresCol() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionSummary
-
Field in "predictions" which gives the features of each instance as a vector.
- featuresCol() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionSummaryImpl
-
- featuresCol() - 类 中的方法org.apache.spark.ml.classification.OneVsRest
-
- featuresCol() - 类 中的方法org.apache.spark.ml.classification.OneVsRestModel
-
- featuresCol() - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeans
-
- featuresCol() - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
- featuresCol() - 类 中的方法org.apache.spark.ml.clustering.ClusteringSummary
-
- featuresCol() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixture
-
- featuresCol() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- featuresCol() - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- featuresCol() - 类 中的方法org.apache.spark.ml.clustering.KMeansModel
-
- featuresCol() - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- featuresCol() - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
- featuresCol() - 类 中的方法org.apache.spark.ml.evaluation.ClusteringEvaluator
-
- featuresCol() - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- featuresCol() - 类 中的方法org.apache.spark.ml.feature.ChiSqSelectorModel
-
- featuresCol() - 类 中的方法org.apache.spark.ml.feature.RFormula
-
- featuresCol() - 类 中的方法org.apache.spark.ml.feature.RFormulaModel
-
- featuresCol() - 接口 中的方法org.apache.spark.ml.param.shared.HasFeaturesCol
-
Param for features column name.
- featuresCol() - 类 中的方法org.apache.spark.ml.PredictionModel
-
- featuresCol() - 类 中的方法org.apache.spark.ml.Predictor
-
- featuresCol() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- featuresCol() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- featuresCol() - 类 中的方法org.apache.spark.ml.regression.IsotonicRegression
-
- featuresCol() - 类 中的方法org.apache.spark.ml.regression.IsotonicRegressionModel
-
- featuresCol() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionSummary
-
- featureSubsetStrategy() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- featureSubsetStrategy() - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- featureSubsetStrategy() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- featureSubsetStrategy() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- featureSubsetStrategy() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- featureSubsetStrategy() - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- featureSubsetStrategy() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- featureSubsetStrategy() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- featureSubsetStrategy() - 接口 中的方法org.apache.spark.ml.tree.TreeEnsembleParams
-
The number of features to consider for splits at each tree node.
- featureSum() - 类 中的方法org.apache.spark.ml.evaluation.SquaredEuclideanSilhouette.ClusterStats
-
- FeatureType - org.apache.spark.mllib.tree.configuration中的类
-
Enum to describe whether a feature is "continuous" or "categorical"
- FeatureType() - 类 的构造器org.apache.spark.mllib.tree.configuration.FeatureType
-
- featureType() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.SplitData
-
- featureType() - 类 中的方法org.apache.spark.mllib.tree.model.Split
-
- FETCH_WAIT_TIME() - 类 中的方法org.apache.spark.InternalAccumulator.shuffleRead$
-
- FetchFailed - org.apache.spark中的类
-
:: DeveloperApi ::
Task failed to fetch shuffle data from a remote node.
- FetchFailed(BlockManagerId, int, long, int, int, String) - 类 的构造器org.apache.spark.FetchFailed
-
- fetchFile(String, File, SparkConf, org.apache.spark.SecurityManager, Configuration, long, boolean) - 类 中的静态方法org.apache.spark.util.Utils
-
Download a file or directory to target directory.
- fetchPct() - 类 中的方法org.apache.spark.scheduler.RuntimePercentage
-
- fetchWaitTime() - 类 中的方法org.apache.spark.status.api.v1.ShuffleReadMetricDistributions
-
- fetchWaitTime() - 类 中的方法org.apache.spark.status.api.v1.ShuffleReadMetrics
-
- field() - 类 中的方法org.apache.spark.storage.BroadcastBlockId
-
- fieldIndex(String) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the index of a given field name.
- fieldIndex(String) - 类 中的方法org.apache.spark.sql.types.StructType
-
Returns the index of a given field.
- fieldNames() - 类 中的方法org.apache.spark.sql.connector.catalog.TableChange.AddColumn
-
- fieldNames() - 接口 中的方法org.apache.spark.sql.connector.catalog.TableChange.ColumnChange
-
- fieldNames() - 类 中的方法org.apache.spark.sql.connector.catalog.TableChange.DeleteColumn
-
- fieldNames() - 类 中的方法org.apache.spark.sql.connector.catalog.TableChange.RenameColumn
-
- fieldNames() - 类 中的方法org.apache.spark.sql.connector.catalog.TableChange.UpdateColumnComment
-
- fieldNames() - 类 中的方法org.apache.spark.sql.connector.catalog.TableChange.UpdateColumnType
-
- fieldNames() - 接口 中的方法org.apache.spark.sql.connector.expressions.NamedReference
-
Returns the referenced field name as an array of String parts.
- fieldNames() - 类 中的方法org.apache.spark.sql.types.StructType
-
Returns all field names in an array.
- fields() - 类 中的方法org.apache.spark.sql.types.StructType
-
- FIFO() - 类 中的静态方法org.apache.spark.scheduler.SchedulingMode
-
- FILE_FORMAT() - 类 中的静态方法org.apache.spark.sql.hive.execution.HiveOptions
-
- FileBasedTopologyMapper - org.apache.spark.storage中的类
-
A simple file based topology mapper.
- FileBasedTopologyMapper(SparkConf) - 类 的构造器org.apache.spark.storage.FileBasedTopologyMapper
-
- FileCommitProtocol - org.apache.spark.internal.io中的类
-
An interface to define how a single Spark job commits its outputs.
- FileCommitProtocol() - 类 的构造器org.apache.spark.internal.io.FileCommitProtocol
-
- FileCommitProtocol.EmptyTaskCommitMessage$ - org.apache.spark.internal.io中的类
-
- FileCommitProtocol.TaskCommitMessage - org.apache.spark.internal.io中的类
-
- fileFormat() - 类 中的方法org.apache.spark.sql.hive.execution.HiveOptions
-
- files() - 类 中的方法org.apache.spark.SparkContext
-
- fileStream(String, Class<K>, Class<V>, Class<F>) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Create an input stream that monitors a Hadoop-compatible filesystem
for new files and reads them using the given key-value types and input format.
- fileStream(String, Class<K>, Class<V>, Class<F>, Function<Path, Boolean>, boolean) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Create an input stream that monitors a Hadoop-compatible filesystem
for new files and reads them using the given key-value types and input format.
- fileStream(String, Class<K>, Class<V>, Class<F>, Function<Path, Boolean>, boolean, Configuration) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Create an input stream that monitors a Hadoop-compatible filesystem
for new files and reads them using the given key-value types and input format.
- fileStream(String, ClassTag<K>, ClassTag<V>, ClassTag<F>) - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Create an input stream that monitors a Hadoop-compatible filesystem
for new files and reads them using the given key-value types and input format.
- fileStream(String, Function1<Path, Object>, boolean, ClassTag<K>, ClassTag<V>, ClassTag<F>) - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Create an input stream that monitors a Hadoop-compatible filesystem
for new files and reads them using the given key-value types and input format.
- fileStream(String, Function1<Path, Object>, boolean, Configuration, ClassTag<K>, ClassTag<V>, ClassTag<F>) - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Create an input stream that monitors a Hadoop-compatible filesystem
for new files and reads them using the given key-value types and input format.
- fill(long) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Returns a new DataFrame that replaces null or NaN values in numeric columns with value.
- fill(double) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Returns a new DataFrame that replaces null or NaN values in numeric columns with value.
- fill(String) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Returns a new DataFrame that replaces null values in string columns with value.
- fill(long, String[]) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Returns a new DataFrame that replaces null or NaN values in specified numeric columns.
- fill(double, String[]) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Returns a new DataFrame that replaces null or NaN values in specified numeric columns.
- fill(long, Seq<String>) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
(Scala-specific) Returns a new DataFrame that replaces null or NaN values in specified
numeric columns.
- fill(double, Seq<String>) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
(Scala-specific) Returns a new DataFrame that replaces null or NaN values in specified
numeric columns.
- fill(String, String[]) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Returns a new DataFrame that replaces null values in specified string columns.
- fill(String, Seq<String>) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
(Scala-specific) Returns a new DataFrame that replaces null values in
specified string columns.
- fill(boolean) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Returns a new DataFrame that replaces null values in boolean columns with value.
- fill(boolean, Seq<String>) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
(Scala-specific) Returns a new DataFrame that replaces null values in specified
boolean columns.
- fill(boolean, String[]) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Returns a new DataFrame that replaces null values in specified boolean columns.
- fill(Map<String, Object>) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Returns a new DataFrame that replaces null values.
- fill(Map<String, Object>) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
(Scala-specific) Returns a new DataFrame that replaces null values.
- filter(Function<Double, Boolean>) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Return a new RDD containing only the elements that satisfy a predicate.
- filter(Function<Tuple2<K, V>, Boolean>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return a new RDD containing only the elements that satisfy a predicate.
- filter(Function<T, Boolean>) - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Return a new RDD containing only the elements that satisfy a predicate.
- filter(Function1<Graph<VD, ED>, Graph<VD2, ED2>>, Function1<EdgeTriplet<VD2, ED2>, Object>, Function2<Object, VD2, Object>, ClassTag<VD2>, ClassTag<ED2>) - 类 中的方法org.apache.spark.graphx.GraphOps
-
Filter the graph by computing some values to filter on, and applying the predicates.
- filter(Function1<EdgeTriplet<VD, ED>, Object>, Function2<Object, VD, Object>) - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
- filter(Function1<Tuple2<Object, VD>, Object>) - 类 中的方法org.apache.spark.graphx.VertexRDD
-
Restricts the vertex set to the set of vertices satisfying the given predicate.
- filter(Params) - 类 中的方法org.apache.spark.ml.param.ParamMap
-
Filters this param map for the given parent.
- filter(Function1<T, Object>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return a new RDD containing only the elements that satisfy a predicate.
- filter(Column) - 类 中的方法org.apache.spark.sql.Dataset
-
Filters rows using the given condition.
- filter(String) - 类 中的方法org.apache.spark.sql.Dataset
-
Filters rows using the given SQL expression.
- filter(Function1<T, Object>) - 类 中的方法org.apache.spark.sql.Dataset
-
(Scala-specific)
Returns a new Dataset that only contains elements where func returns true.
- filter(FilterFunction<T>) - 类 中的方法org.apache.spark.sql.Dataset
-
(Java-specific)
Returns a new Dataset that only contains elements where func returns true.
- filter(Column, Function1<Column, Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns an array of elements for which a predicate holds in a given array.
- filter(Column, Function2<Column, Column, Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns an array of elements for which a predicate holds in a given array.
- Filter - org.apache.spark.sql.sources中的类
-
A filter predicate for data sources.
- Filter() - 类 的构造器org.apache.spark.sql.sources.Filter
-
- filter() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.GetMatchingBlockIds
-
- filter(Function<T, Boolean>) - 类 中的方法org.apache.spark.streaming.api.java.JavaDStream
-
Return a new DStream containing only the elements that satisfy a predicate.
- filter(Function<Tuple2<K, V>, Boolean>) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream containing only the elements that satisfy a predicate.
- filter(Function1<T, Object>) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream containing only the elements that satisfy a predicate.
- filterByRange(K, K) - 类 中的方法org.apache.spark.rdd.OrderedRDDFunctions
-
Returns an RDD containing only the elements in the inclusive range lower to upper.
- FilterFunction<T> - org.apache.spark.api.java.function中的接口
-
Base interface for a function used in Dataset's filter function.
- filterName() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.AddWebUIFilter
-
- filterParams() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.AddWebUIFilter
-
- finalStorageLevel() - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- finalStorageLevel() - 接口 中的方法org.apache.spark.ml.recommendation.ALSParams
-
Param for StorageLevel for ALS model factors.
- findClass(String) - 类 中的方法org.apache.spark.util.ParentClassLoader
-
- findColumnPosition(Seq<String>, StructType, Function2<String, String, Object>) - 类 中的静态方法org.apache.spark.sql.util.SchemaUtils
-
Returns the given column's ordinal within the given schema.
- findExpressionAndTrackLineageDown(Expression, LogicalPlan) - 类 中的静态方法org.apache.spark.sql.dynamicpruning.CleanupDynamicPruningFilters
-
- findExpressionAndTrackLineageDown(Expression, LogicalPlan) - 类 中的静态方法org.apache.spark.sql.dynamicpruning.PartitionPruning
-
- findFrequentSequentialPatterns(Dataset<?>) - 类 中的方法org.apache.spark.ml.fpm.PrefixSpan
-
Finds the complete set of frequent sequential patterns in the input sequences of itemsets.
- findListenersByClass(ClassTag<T>) - 接口 中的方法org.apache.spark.util.ListenerBus
-
- findMatchingTokenClusterConfig(SparkConf, String) - 类 中的静态方法org.apache.spark.kafka010.KafkaTokenUtil
-
- findMissingPartitions() - 类 中的方法org.apache.spark.ShuffleStatus
-
Returns the sequence of partition ids that are missing (i.e. needs to be computed).
- findSynonyms(String, int) - 类 中的方法org.apache.spark.ml.feature.Word2VecModel
-
Find "num" number of words closest in similarity to the given word, not
including the word itself.
- findSynonyms(Vector, int) - 类 中的方法org.apache.spark.ml.feature.Word2VecModel
-
Find "num" number of words whose vector representation is most similar to the supplied vector.
- findSynonyms(String, int) - 类 中的方法org.apache.spark.mllib.feature.Word2VecModel
-
Find synonyms of a word; do not include the word itself in results.
- findSynonyms(Vector, int) - 类 中的方法org.apache.spark.mllib.feature.Word2VecModel
-
Find synonyms of the vector representation of a word, possibly
including any words in the model vocabulary whose vector respresentation
is the supplied vector.
- findSynonymsArray(Vector, int) - 类 中的方法org.apache.spark.ml.feature.Word2VecModel
-
Find "num" number of words whose vector representation is most similar to the supplied vector.
- findSynonymsArray(String, int) - 类 中的方法org.apache.spark.ml.feature.Word2VecModel
-
Find "num" number of words closest in similarity to the given word, not
including the word itself.
- finish(OpenHashMap<String, Object>[]) - 类 中的方法org.apache.spark.ml.feature.StringIndexerAggregator
-
- finish(BUF) - 类 中的方法org.apache.spark.sql.expressions.Aggregator
-
Transform the output of the reduction.
- finished() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
- FINISHED() - 类 中的静态方法org.apache.spark.TaskState
-
- finishTime() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
The time when the task has completed successfully (including the time to remotely fetch
results, if necessary).
- first() - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
- first() - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
- first() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return the first element in this RDD.
- first() - 类 中的方法org.apache.spark.rdd.RDD
-
Return the first element in this RDD.
- first() - 类 中的方法org.apache.spark.sql.Dataset
-
Returns the first row.
- first(Column, boolean) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the first value in a group.
- first(String, boolean) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the first value of a column in a group.
- first(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the first value in a group.
- first(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the first value of a column in a group.
- firstFailureReason() - 类 中的方法org.apache.spark.status.api.v1.streaming.BatchInfo
-
- firstLaunchTime() - 类 中的方法org.apache.spark.status.LiveStage
-
- firstTaskLaunchedTime() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.classification.OneVsRest
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeans
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixture
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- fit(Dataset<?>, ParamPair<?>, ParamPair<?>...) - 类 中的方法org.apache.spark.ml.Estimator
-
Fits a single model to the input data with optional parameters.
- fit(Dataset<?>, ParamPair<?>, Seq<ParamPair<?>>) - 类 中的方法org.apache.spark.ml.Estimator
-
Fits a single model to the input data with optional parameters.
- fit(Dataset<?>, ParamMap) - 类 中的方法org.apache.spark.ml.Estimator
-
Fits a single model to the input data with provided parameter map.
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.Estimator
-
Fits a model to the input data.
- fit(Dataset<?>, ParamMap[]) - 类 中的方法org.apache.spark.ml.Estimator
-
Fits multiple models to the input data with multiple sets of parameters.
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.CountVectorizer
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.IDF
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.Imputer
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.MaxAbsScaler
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.MinMaxScaler
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.OneHotEncoder
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.PCA
-
Computes a
PCAModel that contains the principal components of the input vectors.
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.QuantileDiscretizer
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.RFormula
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.RobustScaler
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.StandardScaler
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.StringIndexer
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.VectorIndexer
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.fpm.FPGrowth
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.Pipeline
-
Fits the pipeline to the input dataset with additional parameters.
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.Predictor
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.regression.IsotonicRegression
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.tuning.CrossValidator
-
- fit(Dataset<?>) - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplit
-
- fit(RDD<LabeledPoint>) - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelector
-
Returns a ChiSquared feature selector.
- fit(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.feature.IDF
-
Computes the inverse document frequency.
- fit(JavaRDD<Vector>) - 类 中的方法org.apache.spark.mllib.feature.IDF
-
Computes the inverse document frequency.
- fit(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.feature.PCA
-
Computes a
PCAModel that contains the principal components of the input vectors.
- fit(JavaRDD<Vector>) - 类 中的方法org.apache.spark.mllib.feature.PCA
-
Java-friendly version of fit().
- fit(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.feature.StandardScaler
-
Computes the mean and variance and stores as a model to be used for later scaling.
- fit(RDD<S>) - 类 中的方法org.apache.spark.mllib.feature.Word2Vec
-
Computes the vector representation of each word in vocabulary.
- fit(JavaRDD<S>) - 类 中的方法org.apache.spark.mllib.feature.Word2Vec
-
Computes the vector representation of each word in vocabulary (Java version).
- FitEnd<M extends Model<M>> - org.apache.spark.ml中的类
-
Event fired after Estimator.fit.
- FitEnd() - 类 的构造器org.apache.spark.ml.FitEnd
-
- fitIntercept() - 类 中的方法org.apache.spark.ml.classification.LinearSVC
-
- fitIntercept() - 类 中的方法org.apache.spark.ml.classification.LinearSVCModel
-
- fitIntercept() - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
- fitIntercept() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- fitIntercept() - 接口 中的方法org.apache.spark.ml.param.shared.HasFitIntercept
-
Param for whether to fit an intercept term.
- fitIntercept() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- fitIntercept() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- fitIntercept() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
- fitIntercept() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
- fitIntercept() - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
- fitIntercept() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionModel
-
- FitStart<M extends Model<M>> - org.apache.spark.ml中的类
-
Event fired before Estimator.fit.
- FitStart() - 类 的构造器org.apache.spark.ml.FitStart
-
- Fixed$() - 类 的构造器org.apache.spark.sql.types.DecimalType.Fixed$
-
- flatMap(FlatMapFunction<T, U>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return a new RDD by first applying a function to all elements of this
RDD, and then flattening the results.
- flatMap(Function1<T, TraversableOnce<U>>, ClassTag<U>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return a new RDD by first applying a function to all elements of this
RDD, and then flattening the results.
- flatMap(Function1<T, TraversableOnce<U>>, Encoder<U>) - 类 中的方法org.apache.spark.sql.Dataset
-
(Scala-specific)
Returns a new Dataset by first applying a function to all elements of this Dataset,
and then flattening the results.
- flatMap(FlatMapFunction<T, U>, Encoder<U>) - 类 中的方法org.apache.spark.sql.Dataset
-
(Java-specific)
Returns a new Dataset by first applying a function to all elements of this Dataset,
and then flattening the results.
- flatMap(FlatMapFunction<T, U>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream by applying a function to all elements of this DStream,
and then flattening the results
- flatMap(Function1<T, TraversableOnce<U>>, ClassTag<U>) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream by applying a function to all elements of this DStream,
and then flattening the results
- FlatMapFunction<T,R> - org.apache.spark.api.java.function中的接口
-
A function that returns zero or more output records from each input record.
- FlatMapFunction2<T1,T2,R> - org.apache.spark.api.java.function中的接口
-
A function that takes two inputs and returns zero or more output records.
- flatMapGroups(Function2<K, Iterator<V>, TraversableOnce<U>>, Encoder<U>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
(Scala-specific)
Applies the given function to each group of data.
- flatMapGroups(FlatMapGroupsFunction<K, V, U>, Encoder<U>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
(Java-specific)
Applies the given function to each group of data.
- FlatMapGroupsFunction<K,V,R> - org.apache.spark.api.java.function中的接口
-
A function that returns zero or more output records from each grouping key and its values.
- flatMapGroupsWithState(OutputMode, GroupStateTimeout, Function3<K, Iterator<V>, GroupState<S>, Iterator<U>>, Encoder<S>, Encoder<U>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
(Scala-specific)
Applies the given function to each group of data, while maintaining a user-defined per-group
state.
- flatMapGroupsWithState(FlatMapGroupsWithStateFunction<K, V, S, U>, OutputMode, Encoder<S>, Encoder<U>, GroupStateTimeout) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
(Java-specific)
Applies the given function to each group of data, while maintaining a user-defined per-group
state.
- FlatMapGroupsWithStateFunction<K,V,S,R> - org.apache.spark.api.java.function中的接口
-
::Experimental::
Base interface for a map function used in
org.apache.spark.sql.KeyValueGroupedDataset.flatMapGroupsWithState(
FlatMapGroupsWithStateFunction, org.apache.spark.sql.streaming.OutputMode,
org.apache.spark.sql.Encoder, org.apache.spark.sql.Encoder)
- flatMapToDouble(DoubleFlatMapFunction<T>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return a new RDD by first applying a function to all elements of this
RDD, and then flattening the results.
- flatMapToPair(PairFlatMapFunction<T, K2, V2>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return a new RDD by first applying a function to all elements of this
RDD, and then flattening the results.
- flatMapToPair(PairFlatMapFunction<T, K2, V2>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream by applying a function to all elements of this DStream,
and then flattening the results
- flatMapValues(FlatMapFunction<V, U>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Pass each value in the key-value pair RDD through a flatMap function without changing the
keys; this also retains the original RDD's partitioning.
- flatMapValues(Function1<V, TraversableOnce<U>>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Pass each value in the key-value pair RDD through a flatMap function without changing the
keys; this also retains the original RDD's partitioning.
- flatMapValues(FlatMapFunction<V, U>) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying a flatmap function to the value of each key-value pairs in
'this' DStream without changing the key.
- flatMapValues(Function1<V, TraversableOnce<U>>, ClassTag<U>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying a flatmap function to the value of each key-value pairs in
'this' DStream without changing the key.
- flatten(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a single array from an array of arrays.
- FLOAT() - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for nullable float type.
- FloatExactNumeric - org.apache.spark.sql.types中的类
-
- FloatExactNumeric() - 类 的构造器org.apache.spark.sql.types.FloatExactNumeric
-
- FloatParam - org.apache.spark.ml.param中的类
-
:: DeveloperApi ::
Specialized version of Param[Float] for Java.
- FloatParam(String, String, String, Function1<Object, Object>) - 类 的构造器org.apache.spark.ml.param.FloatParam
-
- FloatParam(String, String, String) - 类 的构造器org.apache.spark.ml.param.FloatParam
-
- FloatParam(Identifiable, String, String, Function1<Object, Object>) - 类 的构造器org.apache.spark.ml.param.FloatParam
-
- FloatParam(Identifiable, String, String) - 类 的构造器org.apache.spark.ml.param.FloatParam
-
- FloatType - 类 中的静态变量org.apache.spark.sql.types.DataTypes
-
Gets the FloatType object.
- FloatType - org.apache.spark.sql.types中的类
-
The data type representing Float values.
- FloatType() - 类 的构造器org.apache.spark.sql.types.FloatType
-
- floor(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the floor of the given value.
- floor(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the floor of the given column.
- floor() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- floor(Duration) - 类 中的方法org.apache.spark.streaming.Time
-
- floor(Duration, Time) - 类 中的方法org.apache.spark.streaming.Time
-
- flush() - 类 中的方法org.apache.spark.serializer.SerializationStream
-
- flush() - 类 中的方法org.apache.spark.storage.TimeTrackingOutputStream
-
- fMeasure(double, double) - 类 中的方法org.apache.spark.mllib.evaluation.MulticlassMetrics
-
Returns f-measure for a given label (category)
- fMeasure(double) - 类 中的方法org.apache.spark.mllib.evaluation.MulticlassMetrics
-
Returns f1-measure for a given label (category)
- fMeasureByLabel(double) - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionSummary
-
Returns f-measure for each label (category).
- fMeasureByLabel() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionSummary
-
Returns f1-measure for each label (category).
- fMeasureByThreshold() - 接口 中的方法org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
-
Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.
- fMeasureByThreshold() - 类 中的方法org.apache.spark.ml.classification.BinaryLogisticRegressionSummaryImpl
-
- fMeasureByThreshold(double) - 类 中的方法org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
-
Returns the (threshold, F-Measure) curve.
- fMeasureByThreshold() - 类 中的方法org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
-
Returns the (threshold, F-Measure) curve with beta = 1.0.
- fold(T, Function2<T, T, T>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Aggregate the elements of each partition, and then the results for all the partitions, using a
given associative function and a neutral "zero value".
- fold(T, Function2<T, T, T>) - 类 中的方法org.apache.spark.rdd.RDD
-
Aggregate the elements of each partition, and then the results for all the partitions, using a
given associative function and a neutral "zero value".
- foldByKey(V, Partitioner, Function2<V, V, V>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Merge the values for each key using an associative function and a neutral "zero value" which
may be added to the result an arbitrary number of times, and must not change the result
(e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
- foldByKey(V, int, Function2<V, V, V>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Merge the values for each key using an associative function and a neutral "zero value" which
may be added to the result an arbitrary number of times, and must not change the result
(e.g ., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
- foldByKey(V, Function2<V, V, V>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Merge the values for each key using an associative function and a neutral "zero value"
which may be added to the result an arbitrary number of times, and must not change the result
(e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
- foldByKey(V, Partitioner, Function2<V, V, V>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Merge the values for each key using an associative function and a neutral "zero value" which
may be added to the result an arbitrary number of times, and must not change the result
(e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
- foldByKey(V, int, Function2<V, V, V>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Merge the values for each key using an associative function and a neutral "zero value" which
may be added to the result an arbitrary number of times, and must not change the result
(e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
- foldByKey(V, Function2<V, V, V>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Merge the values for each key using an associative function and a neutral "zero value" which
may be added to the result an arbitrary number of times, and must not change the result
(e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
- forall(Column, Function1<Column, Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns whether a predicate holds for every element in the array.
- forceIndexLabel() - 类 中的方法org.apache.spark.ml.feature.RFormula
-
- forceIndexLabel() - 接口 中的方法org.apache.spark.ml.feature.RFormulaBase
-
Force to index label whether it is numeric or string type.
- forceIndexLabel() - 类 中的方法org.apache.spark.ml.feature.RFormulaModel
-
- foreach(VoidFunction<T>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Applies a function f to all elements of this RDD.
- foreach(Function1<T, BoxedUnit>) - 类 中的方法org.apache.spark.rdd.RDD
-
Applies a function f to all elements of this RDD.
- foreach(Function1<T, BoxedUnit>) - 类 中的方法org.apache.spark.sql.Dataset
-
Applies a function f to all rows.
- foreach(ForeachFunction<T>) - 类 中的方法org.apache.spark.sql.Dataset
-
(Java-specific)
Runs func on each element of this Dataset.
- foreach(ForeachWriter<T>) - 类 中的方法org.apache.spark.sql.streaming.DataStreamWriter
-
Sets the output of the streaming query to be processed using the provided writer object.
- foreachActive(Function3<Object, Object, Object, BoxedUnit>) - 类 中的方法org.apache.spark.ml.linalg.DenseMatrix
-
- foreachActive(Function2<Object, Object, BoxedUnit>) - 类 中的方法org.apache.spark.ml.linalg.DenseVector
-
- foreachActive(Function3<Object, Object, Object, BoxedUnit>) - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Applies a function f to all the active elements of dense and sparse matrix.
- foreachActive(Function3<Object, Object, Object, BoxedUnit>) - 类 中的方法org.apache.spark.ml.linalg.SparseMatrix
-
- foreachActive(Function2<Object, Object, BoxedUnit>) - 类 中的方法org.apache.spark.ml.linalg.SparseVector
-
- foreachActive(Function2<Object, Object, BoxedUnit>) - 接口 中的方法org.apache.spark.ml.linalg.Vector
-
Applies a function f to all the active elements of dense and sparse vector.
- foreachActive(Function2<Object, Object, BoxedUnit>) - 类 中的方法org.apache.spark.mllib.linalg.DenseVector
-
- foreachActive(Function3<Object, Object, Object, BoxedUnit>) - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
Applies a function f to all the active elements of dense and sparse matrix.
- foreachActive(Function2<Object, Object, BoxedUnit>) - 类 中的方法org.apache.spark.mllib.linalg.SparseVector
-
- foreachActive(Function2<Object, Object, BoxedUnit>) - 接口 中的方法org.apache.spark.mllib.linalg.Vector
-
Applies a function f to all the active elements of dense and sparse vector.
- foreachAsync(VoidFunction<T>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
The asynchronous version of the foreach action, which
applies a function f to all the elements of this RDD.
- foreachAsync(Function1<T, BoxedUnit>) - 类 中的方法org.apache.spark.rdd.AsyncRDDActions
-
Applies a function f to all elements of this RDD.
- foreachBatch(Function2<Dataset<T>, Object, BoxedUnit>) - 类 中的方法org.apache.spark.sql.streaming.DataStreamWriter
-
:: Experimental ::
(Scala-specific) Sets the output of the streaming query to be processed using the provided
function.
- foreachBatch(VoidFunction2<Dataset<T>, Long>) - 类 中的方法org.apache.spark.sql.streaming.DataStreamWriter
-
:: Experimental ::
(Java-specific) Sets the output of the streaming query to be processed using the provided
function.
- ForeachFunction<T> - org.apache.spark.api.java.function中的接口
-
Base interface for a function used in Dataset's foreach function.
- foreachPartition(VoidFunction<Iterator<T>>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Applies a function f to each partition of this RDD.
- foreachPartition(Function1<Iterator<T>, BoxedUnit>) - 类 中的方法org.apache.spark.rdd.RDD
-
Applies a function f to each partition of this RDD.
- foreachPartition(Function1<Iterator<T>, BoxedUnit>) - 类 中的方法org.apache.spark.sql.Dataset
-
Applies a function f to each partition of this Dataset.
- foreachPartition(ForeachPartitionFunction<T>) - 类 中的方法org.apache.spark.sql.Dataset
-
(Java-specific)
Runs func on each partition of this Dataset.
- foreachPartitionAsync(VoidFunction<Iterator<T>>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
The asynchronous version of the foreachPartition action, which
applies a function f to each partition of this RDD.
- foreachPartitionAsync(Function1<Iterator<T>, BoxedUnit>) - 类 中的方法org.apache.spark.rdd.AsyncRDDActions
-
Applies a function f to each partition of this RDD.
- ForeachPartitionFunction<T> - org.apache.spark.api.java.function中的接口
-
Base interface for a function used in Dataset's foreachPartition function.
- foreachRDD(VoidFunction<R>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Apply a function to each RDD in this DStream.
- foreachRDD(VoidFunction2<R, Time>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Apply a function to each RDD in this DStream.
- foreachRDD(Function1<RDD<T>, BoxedUnit>) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Apply a function to each RDD in this DStream.
- foreachRDD(Function2<RDD<T>, Time, BoxedUnit>) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Apply a function to each RDD in this DStream.
- ForeachWriter<T> - org.apache.spark.sql中的类
-
The abstract class for writing custom logic to process data generated by a query.
- ForeachWriter() - 类 的构造器org.apache.spark.sql.ForeachWriter
-
- format() - 类 中的方法org.apache.spark.ml.clustering.InternalKMeansModelWriter
-
- format() - 类 中的方法org.apache.spark.ml.clustering.PMMLKMeansModelWriter
-
- format() - 类 中的方法org.apache.spark.ml.regression.InternalLinearRegressionModelWriter
-
- format() - 类 中的方法org.apache.spark.ml.regression.PMMLLinearRegressionModelWriter
-
- format(String) - 类 中的方法org.apache.spark.ml.util.GeneralMLWriter
-
Specifies the format of ML export (e.g.
- format() - 接口 中的方法org.apache.spark.ml.util.MLFormatRegister
-
The string that represents the format that this format provider uses.
- format(String) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Specifies the input data source format.
- format(String) - 类 中的方法org.apache.spark.sql.DataFrameWriter
-
Specifies the underlying output data source.
- format(String) - 类 中的方法org.apache.spark.sql.streaming.DataStreamReader
-
Specifies the input data source format.
- format(String) - 类 中的方法org.apache.spark.sql.streaming.DataStreamWriter
-
Specifies the underlying output data source.
- format_number(Column, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Formats numeric column x to a format like '#,###,###.##', rounded to d decimal places
with HALF_EVEN round mode, and returns the result as a string column.
- format_string(String, Column...) - 类 中的静态方法org.apache.spark.sql.functions
-
Formats the arguments in printf-style and returns the result as a string column.
- format_string(String, Seq<Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Formats the arguments in printf-style and returns the result as a string column.
- formatBatchTime(long, long, boolean, TimeZone) - 类 中的静态方法org.apache.spark.streaming.ui.UIUtils
-
If batchInterval is less than 1 second, format batchTime with milliseconds.
- formatDate(Date) - 类 中的静态方法org.apache.spark.ui.UIUtils
-
- formatDate(long) - 类 中的静态方法org.apache.spark.ui.UIUtils
-
- formatDuration(long) - 类 中的静态方法org.apache.spark.ui.UIUtils
-
- formatDurationVerbose(long) - 类 中的静态方法org.apache.spark.ui.UIUtils
-
Generate a verbose human-readable string representing a duration such as "5 second 35 ms"
- formatNumber(double) - 类 中的静态方法org.apache.spark.ui.UIUtils
-
Generate a human-readable string representing a number (e.g. 100 K)
- formula() - 类 中的方法org.apache.spark.ml.feature.RFormula
-
- formula() - 接口 中的方法org.apache.spark.ml.feature.RFormulaBase
-
R formula parameter.
- formula() - 类 中的方法org.apache.spark.ml.feature.RFormulaModel
-
- forward(DenseMatrix<Object>, boolean) - 接口 中的方法org.apache.spark.ml.ann.TopologyModel
-
Forward propagation
- FPGA() - 类 中的静态方法org.apache.spark.resource.ResourceUtils
-
- FPGrowth - org.apache.spark.ml.fpm中的类
-
A parallel FP-growth algorithm to mine frequent itemsets.
- FPGrowth(String) - 类 的构造器org.apache.spark.ml.fpm.FPGrowth
-
- FPGrowth() - 类 的构造器org.apache.spark.ml.fpm.FPGrowth
-
- FPGrowth - org.apache.spark.mllib.fpm中的类
-
A parallel FP-growth algorithm to mine frequent itemsets.
- FPGrowth() - 类 的构造器org.apache.spark.mllib.fpm.FPGrowth
-
Constructs a default instance with default parameters {minSupport: 0.3, numPartitions: same
as the input data}.
- FPGrowth.FreqItemset<Item> - org.apache.spark.mllib.fpm中的类
-
Frequent itemset.
- FPGrowthModel - org.apache.spark.ml.fpm中的类
-
Model fitted by FPGrowth.
- FPGrowthModel<Item> - org.apache.spark.mllib.fpm中的类
-
Model trained by
FPGrowth, which holds frequent itemsets.
- FPGrowthModel(RDD<FPGrowth.FreqItemset<Item>>, Map<Item, Object>, ClassTag<Item>) - 类 的构造器org.apache.spark.mllib.fpm.FPGrowthModel
-
- FPGrowthModel(RDD<FPGrowth.FreqItemset<Item>>, ClassTag<Item>) - 类 的构造器org.apache.spark.mllib.fpm.FPGrowthModel
-
- FPGrowthModel.SaveLoadV1_0$ - org.apache.spark.mllib.fpm中的类
-
- FPGrowthParams - org.apache.spark.ml.fpm中的接口
-
Common params for FPGrowth and FPGrowthModel
- fpr() - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- fpr() - 类 中的方法org.apache.spark.ml.feature.ChiSqSelectorModel
-
- fpr() - 接口 中的方法org.apache.spark.ml.feature.ChiSqSelectorParams
-
The highest p-value for features to be kept.
- fpr() - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelector
-
- FRACTION_CACHED() - 类 中的静态方法org.apache.spark.ui.storage.ToolTips
-
- freq() - 类 中的方法org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
-
- freq() - 类 中的方法org.apache.spark.mllib.fpm.PrefixSpan.FreqSequence
-
- freqItems(String[], double) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
Finding frequent items for columns, possibly with false positives.
- freqItems(String[]) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
Finding frequent items for columns, possibly with false positives.
- freqItems(Seq<String>, double) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
(Scala-specific) Finding frequent items for columns, possibly with false positives.
- freqItems(Seq<String>) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
(Scala-specific) Finding frequent items for columns, possibly with false positives.
- FreqItemset(Object, long) - 类 的构造器org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
-
- freqItemsets() - 类 中的方法org.apache.spark.ml.fpm.FPGrowthModel
-
- freqItemsets() - 类 中的方法org.apache.spark.mllib.fpm.FPGrowthModel
-
- FreqSequence(Object[], long) - 类 的构造器org.apache.spark.mllib.fpm.PrefixSpan.FreqSequence
-
- freqSequences() - 类 中的方法org.apache.spark.mllib.fpm.PrefixSpanModel
-
- from_csv(Column, StructType, Map<String, String>) - 类 中的静态方法org.apache.spark.sql.functions
-
Parses a column containing a CSV string into a StructType with the specified schema.
- from_csv(Column, Column, Map<String, String>) - 类 中的静态方法org.apache.spark.sql.functions
-
(Java-specific) Parses a column containing a CSV string into a StructType
with the specified schema.
- from_json(Column, StructType, Map<String, String>) - 类 中的静态方法org.apache.spark.sql.functions
-
(Scala-specific) Parses a column containing a JSON string into a StructType with the
specified schema.
- from_json(Column, DataType, Map<String, String>) - 类 中的静态方法org.apache.spark.sql.functions
-
(Scala-specific) Parses a column containing a JSON string into a MapType with StringType
as keys type, StructType or ArrayType with the specified schema.
- from_json(Column, StructType, Map<String, String>) - 类 中的静态方法org.apache.spark.sql.functions
-
(Java-specific) Parses a column containing a JSON string into a StructType with the
specified schema.
- from_json(Column, DataType, Map<String, String>) - 类 中的静态方法org.apache.spark.sql.functions
-
(Java-specific) Parses a column containing a JSON string into a MapType with StringType
as keys type, StructType or ArrayType with the specified schema.
- from_json(Column, StructType) - 类 中的静态方法org.apache.spark.sql.functions
-
Parses a column containing a JSON string into a StructType with the specified schema.
- from_json(Column, DataType) - 类 中的静态方法org.apache.spark.sql.functions
-
Parses a column containing a JSON string into a MapType with StringType as keys type,
StructType or ArrayType with the specified schema.
- from_json(Column, String, Map<String, String>) - 类 中的静态方法org.apache.spark.sql.functions
-
(Java-specific) Parses a column containing a JSON string into a MapType with StringType
as keys type, StructType or ArrayType with the specified schema.
- from_json(Column, String, Map<String, String>) - 类 中的静态方法org.apache.spark.sql.functions
-
(Scala-specific) Parses a column containing a JSON string into a MapType with StringType
as keys type, StructType or ArrayType with the specified schema.
- from_json(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
(Scala-specific) Parses a column containing a JSON string into a MapType with StringType
as keys type, StructType or ArrayType of StructTypes with the specified schema.
- from_json(Column, Column, Map<String, String>) - 类 中的静态方法org.apache.spark.sql.functions
-
(Java-specific) Parses a column containing a JSON string into a MapType with StringType
as keys type, StructType or ArrayType of StructTypes with the specified schema.
- from_unixtime(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string
representing the timestamp of that moment in the current system time zone in the
uuuu-MM-dd HH:mm:ss format.
- from_unixtime(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string
representing the timestamp of that moment in the current system time zone in the given
format.
- from_utc_timestamp(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
- from_utc_timestamp(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
- fromArrowField(Field) - 类 中的静态方法org.apache.spark.sql.util.ArrowUtils
-
- fromArrowSchema(Schema) - 类 中的静态方法org.apache.spark.sql.util.ArrowUtils
-
- fromArrowType(ArrowType) - 类 中的静态方法org.apache.spark.sql.util.ArrowUtils
-
- fromCOO(int, int, Iterable<Tuple3<Object, Object, Object>>) - 类 中的静态方法org.apache.spark.ml.linalg.SparseMatrix
-
Generate a SparseMatrix from Coordinate List (COO) format.
- fromCOO(int, int, Iterable<Tuple3<Object, Object, Object>>) - 类 中的静态方法org.apache.spark.mllib.linalg.SparseMatrix
-
Generate a SparseMatrix from Coordinate List (COO) format.
- fromDDL(String) - 类 中的静态方法org.apache.spark.sql.types.DataType
-
- fromDDL(String) - 类 中的静态方法org.apache.spark.sql.types.StructType
-
Creates StructType for a given DDL-formatted string, which is a comma separated list of field
definitions, e.g., a INT, b STRING.
- fromDecimal(Object) - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- fromDStream(DStream<T>, ClassTag<T>) - 类 中的静态方法org.apache.spark.streaming.api.java.JavaDStream
-
- fromEdgePartitions(RDD<Tuple2<Object, EdgePartition<ED, VD>>>, VD, StorageLevel, StorageLevel, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.impl.GraphImpl
-
Create a graph from EdgePartitions, setting referenced vertices to defaultVertexAttr.
- fromEdges(RDD<Edge<ED>>, ClassTag<ED>, ClassTag<VD>) - 类 中的静态方法org.apache.spark.graphx.EdgeRDD
-
Creates an EdgeRDD from a set of edges.
- fromEdges(RDD<Edge<ED>>, VD, StorageLevel, StorageLevel, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.Graph
-
Construct a graph from a collection of edges.
- fromEdges(EdgeRDD<?>, int, VD, ClassTag<VD>) - 类 中的静态方法org.apache.spark.graphx.VertexRDD
-
Constructs a VertexRDD containing all vertices referred to in edges.
- fromEdgeTuples(RDD<Tuple2<Object, Object>>, VD, Option<PartitionStrategy>, StorageLevel, StorageLevel, ClassTag<VD>) - 类 中的静态方法org.apache.spark.graphx.Graph
-
Construct a graph from a collection of edges encoded as vertex id pairs.
- fromExistingRDDs(VertexRDD<VD>, EdgeRDD<ED>, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.impl.GraphImpl
-
Create a graph from a VertexRDD and an EdgeRDD with the same replicated vertex type as the
vertices.
- fromInputDStream(InputDStream<T>, ClassTag<T>) - 类 中的静态方法org.apache.spark.streaming.api.java.JavaInputDStream
-
- fromInputDStream(InputDStream<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>) - 类 中的静态方法org.apache.spark.streaming.api.java.JavaPairInputDStream
-
- fromInt(int) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- fromInt(int) - 接口 中的方法org.apache.spark.sql.types.Decimal.DecimalIsConflicted
-
- fromInt(int) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- fromInt(int) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- fromInt(int) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- fromInt(int) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- fromInt(int) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- fromInt(int) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- fromJavaDStream(JavaDStream<Tuple2<K, V>>) - 类 中的静态方法org.apache.spark.streaming.api.java.JavaPairDStream
-
- fromJavaRDD(JavaRDD<Tuple2<K, V>>) - 类 中的静态方法org.apache.spark.api.java.JavaPairRDD
-
Convert a JavaRDD of key-value pairs to JavaPairRDD.
- fromJson(String) - 类 中的静态方法org.apache.spark.ml.linalg.JsonMatrixConverter
-
Parses the JSON representation of a Matrix into a
Matrix.
- fromJson(String) - 类 中的静态方法org.apache.spark.ml.linalg.JsonVectorConverter
-
Parses the JSON representation of a vector into a
Vector.
- fromJson(String) - 类 中的静态方法org.apache.spark.mllib.linalg.Vectors
-
Parses the JSON representation of a vector into a
Vector.
- fromJson(String) - 类 中的静态方法org.apache.spark.sql.types.DataType
-
- fromJson(String) - 类 中的静态方法org.apache.spark.sql.types.Metadata
-
Creates a Metadata instance from JSON.
- fromKinesisInitialPosition(InitialPositionInStream) - 类 中的静态方法org.apache.spark.streaming.kinesis.KinesisInitialPositions
-
Returns instance of [[KinesisInitialPosition]] based on the passed
[[InitialPositionInStream]].
- fromMetadata(Metadata) - 接口 中的方法org.apache.spark.ml.attribute.AttributeFactory
-
Creates an
Attribute from a
Metadata instance.
- fromML(DenseMatrix) - 类 中的静态方法org.apache.spark.mllib.linalg.DenseMatrix
-
Convert new linalg type to spark.mllib type.
- fromML(DenseVector) - 类 中的静态方法org.apache.spark.mllib.linalg.DenseVector
-
Convert new linalg type to spark.mllib type.
- fromML(Matrix) - 类 中的静态方法org.apache.spark.mllib.linalg.Matrices
-
Convert new linalg type to spark.mllib type.
- fromML(SparseMatrix) - 类 中的静态方法org.apache.spark.mllib.linalg.SparseMatrix
-
Convert new linalg type to spark.mllib type.
- fromML(SparseVector) - 类 中的静态方法org.apache.spark.mllib.linalg.SparseVector
-
Convert new linalg type to spark.mllib type.
- fromML(Vector) - 类 中的静态方法org.apache.spark.mllib.linalg.Vectors
-
Convert new linalg type to spark.mllib type.
- fromName(String) - 类 中的静态方法org.apache.spark.ml.attribute.AttributeType
-
- fromNullable(T) - 类 中的静态方法org.apache.spark.api.java.Optional
-
- fromOld(Node, Map<Object, Object>) - 类 中的静态方法org.apache.spark.ml.tree.Node
-
Create a new Node from the old Node format, recursively creating child nodes as needed.
- fromPairDStream(DStream<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>) - 类 中的静态方法org.apache.spark.streaming.api.java.JavaPairDStream
-
- fromPairRDD(RDD<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>) - 类 中的静态方法org.apache.spark.mllib.rdd.MLPairRDDFunctions
-
Implicit conversion from a pair RDD to MLPairRDDFunctions.
- fromParams(GeneralizedLinearRegressionBase) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Family$
-
Gets the Family object based on param family and variancePower.
- fromParams(GeneralizedLinearRegressionBase) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Link$
-
Gets the Link object based on param family, link and linkPower.
- fromRDD(RDD<Object>) - 类 中的静态方法org.apache.spark.api.java.JavaDoubleRDD
-
- fromRDD(RDD<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>) - 类 中的静态方法org.apache.spark.api.java.JavaPairRDD
-
- fromRDD(RDD<T>, ClassTag<T>) - 类 中的静态方法org.apache.spark.api.java.JavaRDD
-
- fromRDD(RDD<T>, ClassTag<T>) - 类 中的静态方法org.apache.spark.mllib.rdd.RDDFunctions
-
Implicit conversion from an RDD to RDDFunctions.
- fromRdd(RDD<?>) - 类 中的静态方法org.apache.spark.storage.RDDInfo
-
- fromReceiverInputDStream(ReceiverInputDStream<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>) - 类 中的静态方法org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream
-
- fromReceiverInputDStream(ReceiverInputDStream<T>, ClassTag<T>) - 类 中的静态方法org.apache.spark.streaming.api.java.JavaReceiverInputDStream
-
- fromSparkContext(SparkContext) - 类 中的静态方法org.apache.spark.api.java.JavaSparkContext
-
- fromStage(Stage, int, Option<Object>, TaskMetrics, Seq<Seq<TaskLocation>>) - 类 中的静态方法org.apache.spark.scheduler.StageInfo
-
Construct a StageInfo from a Stage.
- fromString(String) - 枚举 中的静态方法org.apache.spark.JobExecutionStatus
-
- fromString(String) - 类 中的静态方法org.apache.spark.mllib.tree.impurity.Impurities
-
- fromString(String) - 类 中的静态方法org.apache.spark.mllib.tree.loss.Losses
-
- fromString(String) - 枚举 中的静态方法org.apache.spark.status.api.v1.ApplicationStatus
-
- fromString(String) - 枚举 中的静态方法org.apache.spark.status.api.v1.StageStatus
-
- fromString(String) - 枚举 中的静态方法org.apache.spark.status.api.v1.streaming.BatchStatus
-
- fromString(String) - 枚举 中的静态方法org.apache.spark.status.api.v1.TaskSorting
-
- fromString(String) - 类 中的静态方法org.apache.spark.storage.StorageLevel
-
:: DeveloperApi ::
Return the StorageLevel object with the specified name.
- fromStructField(StructField) - 类 中的静态方法org.apache.spark.ml.attribute.Attribute
-
- fromStructField(StructField) - 接口 中的方法org.apache.spark.ml.attribute.AttributeFactory
-
Creates an
Attribute from a
StructField instance.
- fromStructField(StructField) - 类 中的静态方法org.apache.spark.ml.attribute.AttributeGroup
-
Creates an attribute group from a StructField instance.
- fromStructField(StructField) - 类 中的静态方法org.apache.spark.ml.attribute.BinaryAttribute
-
- fromStructField(StructField) - 类 中的静态方法org.apache.spark.ml.attribute.NominalAttribute
-
- fromStructField(StructField) - 类 中的静态方法org.apache.spark.ml.attribute.NumericAttribute
-
- fullOuterJoin(JavaPairRDD<K, W>, Partitioner) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Perform a full outer join of this and other.
- fullOuterJoin(JavaPairRDD<K, W>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Perform a full outer join of this and other.
- fullOuterJoin(JavaPairRDD<K, W>, int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Perform a full outer join of this and other.
- fullOuterJoin(RDD<Tuple2<K, W>>, Partitioner) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Perform a full outer join of this and other.
- fullOuterJoin(RDD<Tuple2<K, W>>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Perform a full outer join of this and other.
- fullOuterJoin(RDD<Tuple2<K, W>>, int) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Perform a full outer join of this and other.
- fullOuterJoin(JavaPairDStream<K, W>) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying 'full outer join' between RDDs of this DStream and
other DStream.
- fullOuterJoin(JavaPairDStream<K, W>, int) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying 'full outer join' between RDDs of this DStream and
other DStream.
- fullOuterJoin(JavaPairDStream<K, W>, Partitioner) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying 'full outer join' between RDDs of this DStream and
other DStream.
- fullOuterJoin(DStream<Tuple2<K, W>>, ClassTag<W>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying 'full outer join' between RDDs of this DStream and
other DStream.
- fullOuterJoin(DStream<Tuple2<K, W>>, int, ClassTag<W>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying 'full outer join' between RDDs of this DStream and
other DStream.
- fullOuterJoin(DStream<Tuple2<K, W>>, Partitioner, ClassTag<W>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying 'full outer join' between RDDs of this DStream and
other DStream.
- fullStackTrace() - 类 中的方法org.apache.spark.ExceptionFailure
-
- Function<T1,R> - org.apache.spark.api.java.function中的接口
-
Base interface for functions whose return types do not create special RDDs.
- Function - org.apache.spark.sql.catalog中的类
-
A user-defined function in Spark, as returned by
listFunctions method in
Catalog.
- Function(String, String, String, String, boolean) - 类 的构造器org.apache.spark.sql.catalog.Function
-
- function(Function4<Time, KeyType, Option<ValueType>, State<StateType>, Option<MappedType>>) - 类 中的静态方法org.apache.spark.streaming.StateSpec
-
- function(Function3<KeyType, Option<ValueType>, State<StateType>, MappedType>) - 类 中的静态方法org.apache.spark.streaming.StateSpec
-
- function(Function4<Time, KeyType, Optional<ValueType>, State<StateType>, Optional<MappedType>>) - 类 中的静态方法org.apache.spark.streaming.StateSpec
-
- function(Function3<KeyType, Optional<ValueType>, State<StateType>, MappedType>) - 类 中的静态方法org.apache.spark.streaming.StateSpec
-
- Function0<R> - org.apache.spark.api.java.function中的接口
-
A zero-argument function that returns an R.
- Function2<T1,T2,R> - org.apache.spark.api.java.function中的接口
-
A two-argument function that takes arguments of type T1 and T2 and returns an R.
- Function3<T1,T2,T3,R> - org.apache.spark.api.java.function中的接口
-
A three-argument function that takes arguments of type T1, T2 and T3 and returns an R.
- Function4<T1,T2,T3,T4,R> - org.apache.spark.api.java.function中的接口
-
A four-argument function that takes arguments of type T1, T2, T3 and T4 and returns an R.
- functionExists(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Check if the function with the specified name exists.
- functionExists(String, String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Check if the function with the specified name exists in the specified database.
- functionExists(String, String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Return whether a function exists in the specified database.
- functions - org.apache.spark.sql中的类
-
Commonly used functions available for DataFrame operations.
- functions() - 类 的构造器org.apache.spark.sql.functions
-
- FutureAction<T> - org.apache.spark中的接口
-
A future for the result of an action to support cancellation.
- futureExecutionContext() - 类 中的静态方法org.apache.spark.rdd.AsyncRDDActions
-
- fwe() - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- fwe() - 类 中的方法org.apache.spark.ml.feature.ChiSqSelectorModel
-
- fwe() - 接口 中的方法org.apache.spark.ml.feature.ChiSqSelectorParams
-
The upper bound of the expected family-wise error rate.
- fwe() - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelector
-
- gain() - 类 中的方法org.apache.spark.ml.tree.DecisionTreeModelReadWrite.NodeData
-
- gain() - 类 中的方法org.apache.spark.ml.tree.InternalNode
-
- gain() - 类 中的方法org.apache.spark.mllib.tree.model.InformationGainStats
-
- Gamma$() - 类 的构造器org.apache.spark.ml.regression.GeneralizedLinearRegression.Gamma$
-
- gamma1() - 类 中的方法org.apache.spark.graphx.lib.SVDPlusPlus.Conf
-
- gamma2() - 类 中的方法org.apache.spark.graphx.lib.SVDPlusPlus.Conf
-
- gamma6() - 类 中的方法org.apache.spark.graphx.lib.SVDPlusPlus.Conf
-
- gamma7() - 类 中的方法org.apache.spark.graphx.lib.SVDPlusPlus.Conf
-
- GammaGenerator - org.apache.spark.mllib.random中的类
-
:: DeveloperApi ::
Generates i.i.d. samples from the gamma distribution with the given shape and scale.
- GammaGenerator(double, double) - 类 的构造器org.apache.spark.mllib.random.GammaGenerator
-
- gammaJavaRDD(JavaSparkContext, double, double, long, int, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
Java-friendly version of RandomRDDs.gammaRDD.
- gammaJavaRDD(JavaSparkContext, double, double, long, int) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
RandomRDDs.gammaJavaRDD with the default seed.
- gammaJavaRDD(JavaSparkContext, double, double, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
RandomRDDs.gammaJavaRDD with the default number of partitions and the default seed.
- gammaJavaVectorRDD(JavaSparkContext, double, double, long, int, int, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
Java-friendly version of RandomRDDs.gammaVectorRDD.
- gammaJavaVectorRDD(JavaSparkContext, double, double, long, int, int) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
RandomRDDs.gammaJavaVectorRDD with the default seed.
- gammaJavaVectorRDD(JavaSparkContext, double, double, long, int) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
RandomRDDs.gammaJavaVectorRDD with the default number of partitions and the default seed.
- gammaRDD(SparkContext, double, double, long, int, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
Generates an RDD comprised of i.i.d.
- gammaVectorRDD(SparkContext, double, double, long, int, int, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
Generates an RDD[Vector] with vectors containing i.i.d.
- gapply(RelationalGroupedDataset, byte[], byte[], Object[], StructType) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
The helper function for gapply() on R side.
- gaps() - 类 中的方法org.apache.spark.ml.feature.RegexTokenizer
-
Indicates whether regex splits on gaps (true) or matches tokens (false).
- GarbageCollectionMetrics - org.apache.spark.metrics中的类
-
- GarbageCollectionMetrics() - 类 的构造器org.apache.spark.metrics.GarbageCollectionMetrics
-
- GAUGE() - 类 中的静态方法org.apache.spark.metrics.sink.StatsdMetricType
-
- Gaussian$() - 类 的构造器org.apache.spark.ml.regression.GeneralizedLinearRegression.Gaussian$
-
- GaussianMixture - org.apache.spark.ml.clustering中的类
-
Gaussian Mixture clustering.
- GaussianMixture(String) - 类 的构造器org.apache.spark.ml.clustering.GaussianMixture
-
- GaussianMixture() - 类 的构造器org.apache.spark.ml.clustering.GaussianMixture
-
- GaussianMixture - org.apache.spark.mllib.clustering中的类
-
This class performs expectation maximization for multivariate Gaussian
Mixture Models (GMMs).
- GaussianMixture() - 类 的构造器org.apache.spark.mllib.clustering.GaussianMixture
-
Constructs a default instance.
- GaussianMixtureModel - org.apache.spark.ml.clustering中的类
-
Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points
are drawn from each Gaussian i with probability weights(i).
- GaussianMixtureModel - org.apache.spark.mllib.clustering中的类
-
Multivariate Gaussian Mixture Model (GMM) consisting of k Gaussians, where points
are drawn from each Gaussian i=1..k with probability w(i); mu(i) and sigma(i) are
the respective mean and covariance for each Gaussian distribution i=1..k.
- GaussianMixtureModel(double[], MultivariateGaussian[]) - 类 的构造器org.apache.spark.mllib.clustering.GaussianMixtureModel
-
- GaussianMixtureParams - org.apache.spark.ml.clustering中的接口
-
Common params for GaussianMixture and GaussianMixtureModel
- GaussianMixtureSummary - org.apache.spark.ml.clustering中的类
-
Summary of GaussianMixture.
- gaussians() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- gaussians() - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixtureModel
-
- gaussiansDF() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
Retrieve Gaussian distributions as a DataFrame.
- GBTClassificationModel - org.apache.spark.ml.classification中的类
-
Gradient-Boosted Trees (GBTs) (http://en.wikipedia.org/wiki/Gradient_boosting)
model for classification.
- GBTClassificationModel(String, DecisionTreeRegressionModel[], double[]) - 类 的构造器org.apache.spark.ml.classification.GBTClassificationModel
-
Construct a GBTClassificationModel
- GBTClassifier - org.apache.spark.ml.classification中的类
-
Gradient-Boosted Trees (GBTs) (http://en.wikipedia.org/wiki/Gradient_boosting)
learning algorithm for classification.
- GBTClassifier(String) - 类 的构造器org.apache.spark.ml.classification.GBTClassifier
-
- GBTClassifier() - 类 的构造器org.apache.spark.ml.classification.GBTClassifier
-
- GBTClassifierParams - org.apache.spark.ml.tree中的接口
-
- GBTParams - org.apache.spark.ml.tree中的接口
-
Parameters for Gradient-Boosted Tree algorithms.
- GBTRegressionModel - org.apache.spark.ml.regression中的类
-
- GBTRegressionModel(String, DecisionTreeRegressionModel[], double[]) - 类 的构造器org.apache.spark.ml.regression.GBTRegressionModel
-
Construct a GBTRegressionModel
- GBTRegressor - org.apache.spark.ml.regression中的类
-
- GBTRegressor(String) - 类 的构造器org.apache.spark.ml.regression.GBTRegressor
-
- GBTRegressor() - 类 的构造器org.apache.spark.ml.regression.GBTRegressor
-
- GBTRegressorParams - org.apache.spark.ml.tree中的接口
-
- GC_TIME() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- GC_TIME() - 类 中的静态方法org.apache.spark.ui.ToolTips
-
- gemm(double, Matrix, DenseMatrix, double, DenseMatrix) - 类 中的静态方法org.apache.spark.ml.linalg.BLAS
-
C := alpha * A * B + beta * C
- gemm(double, Matrix, DenseMatrix, double, DenseMatrix) - 类 中的静态方法org.apache.spark.mllib.linalg.BLAS
-
C := alpha * A * B + beta * C
- gemv(double, Matrix, Vector, double, DenseVector) - 类 中的静态方法org.apache.spark.ml.linalg.BLAS
-
y := alpha * A * x + beta * y
- gemv(double, Matrix, Vector, double, DenseVector) - 类 中的静态方法org.apache.spark.mllib.linalg.BLAS
-
y := alpha * A * x + beta * y
- GeneralizedLinearAlgorithm<M extends GeneralizedLinearModel> - org.apache.spark.mllib.regression中的类
-
:: DeveloperApi ::
GeneralizedLinearAlgorithm implements methods to train a Generalized Linear Model (GLM).
- GeneralizedLinearAlgorithm() - 类 的构造器org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
-
- GeneralizedLinearModel - org.apache.spark.mllib.regression中的类
-
:: DeveloperApi ::
GeneralizedLinearModel (GLM) represents a model trained using
GeneralizedLinearAlgorithm.
- GeneralizedLinearModel(Vector, double) - 类 的构造器org.apache.spark.mllib.regression.GeneralizedLinearModel
-
- GeneralizedLinearRegression - org.apache.spark.ml.regression中的类
-
Fit a Generalized Linear Model
(see
Generalized linear model (Wikipedia))
specified by giving a symbolic description of the linear
predictor (link function) and a description of the error distribution (family).
- GeneralizedLinearRegression(String) - 类 的构造器org.apache.spark.ml.regression.GeneralizedLinearRegression
-
- GeneralizedLinearRegression() - 类 的构造器org.apache.spark.ml.regression.GeneralizedLinearRegression
-
- GeneralizedLinearRegression.Binomial$ - org.apache.spark.ml.regression中的类
-
Binomial exponential family distribution.
- GeneralizedLinearRegression.CLogLog$ - org.apache.spark.ml.regression中的类
-
- GeneralizedLinearRegression.Family$ - org.apache.spark.ml.regression中的类
-
- GeneralizedLinearRegression.FamilyAndLink$ - org.apache.spark.ml.regression中的类
-
- GeneralizedLinearRegression.Gamma$ - org.apache.spark.ml.regression中的类
-
Gamma exponential family distribution.
- GeneralizedLinearRegression.Gaussian$ - org.apache.spark.ml.regression中的类
-
Gaussian exponential family distribution.
- GeneralizedLinearRegression.Identity$ - org.apache.spark.ml.regression中的类
-
- GeneralizedLinearRegression.Inverse$ - org.apache.spark.ml.regression中的类
-
- GeneralizedLinearRegression.Link$ - org.apache.spark.ml.regression中的类
-
- GeneralizedLinearRegression.Log$ - org.apache.spark.ml.regression中的类
-
- GeneralizedLinearRegression.Logit$ - org.apache.spark.ml.regression中的类
-
- GeneralizedLinearRegression.Poisson$ - org.apache.spark.ml.regression中的类
-
Poisson exponential family distribution.
- GeneralizedLinearRegression.Probit$ - org.apache.spark.ml.regression中的类
-
- GeneralizedLinearRegression.Sqrt$ - org.apache.spark.ml.regression中的类
-
- GeneralizedLinearRegression.Tweedie$ - org.apache.spark.ml.regression中的类
-
- GeneralizedLinearRegressionBase - org.apache.spark.ml.regression中的接口
-
Params for Generalized Linear Regression.
- GeneralizedLinearRegressionModel - org.apache.spark.ml.regression中的类
-
- GeneralizedLinearRegressionSummary - org.apache.spark.ml.regression中的类
-
- GeneralizedLinearRegressionTrainingSummary - org.apache.spark.ml.regression中的类
-
- GeneralMLWritable - org.apache.spark.ml.util中的接口
-
Trait for classes that provide GeneralMLWriter.
- GeneralMLWriter - org.apache.spark.ml.util中的类
-
A ML Writer which delegates based on the requested format.
- GeneralMLWriter(PipelineStage) - 类 的构造器org.apache.spark.ml.util.GeneralMLWriter
-
- generateAssociationRules(double) - 类 中的方法org.apache.spark.mllib.fpm.FPGrowthModel
-
Generates association rules for the Items in freqItemsets.
- generateKMeansRDD(SparkContext, int, int, int, double, int) - 类 中的静态方法org.apache.spark.mllib.util.KMeansDataGenerator
-
Generate an RDD containing test data for KMeans.
- generateLinearInput(double, double[], int, int, double) - 类 中的静态方法org.apache.spark.mllib.util.LinearDataGenerator
-
For compatibility, the generated data without specifying the mean and variance
will have zero mean and variance of (1.0/3.0) since the original output range is
[-1, 1] with uniform distribution, and the variance of uniform distribution
is (b - a)^2^ / 12 which will be (1.0/3.0)
- generateLinearInput(double, double[], double[], double[], int, int, double) - 类 中的静态方法org.apache.spark.mllib.util.LinearDataGenerator
-
- generateLinearInput(double, double[], double[], double[], int, int, double, double) - 类 中的静态方法org.apache.spark.mllib.util.LinearDataGenerator
-
- generateLinearInputAsList(double, double[], int, int, double) - 类 中的静态方法org.apache.spark.mllib.util.LinearDataGenerator
-
Return a Java List of synthetic data randomly generated according to a multi
collinear model.
- generateLinearRDD(SparkContext, int, int, double, int, double) - 类 中的静态方法org.apache.spark.mllib.util.LinearDataGenerator
-
Generate an RDD containing sample data for Linear Regression models - including Ridge, Lasso,
and unregularized variants.
- generateLogisticRDD(SparkContext, int, int, double, int, double) - 类 中的静态方法org.apache.spark.mllib.util.LogisticRegressionDataGenerator
-
Generate an RDD containing test data for LogisticRegression.
- generateRandomEdges(int, int, int, long) - 类 中的静态方法org.apache.spark.graphx.util.GraphGenerators
-
- generateRolledOverFileSuffix() - 接口 中的方法org.apache.spark.util.logging.RollingPolicy
-
Get the desired name of the rollover file
- geq(Object) - 类 中的方法org.apache.spark.sql.Column
-
Greater than or equal to an expression.
- get(Object) - 类 中的方法org.apache.spark.api.java.JavaUtils.SerializableMapWrapper
-
- get() - 类 中的方法org.apache.spark.api.java.Optional
-
- get() - 类 中的静态方法org.apache.spark.BarrierTaskContext
-
:: Experimental ::
Returns the currently active BarrierTaskContext.
- get() - 接口 中的方法org.apache.spark.FutureAction
-
Blocks and returns the result of this job.
- get(String) - 接口 中的方法org.apache.spark.internal.config.ConfigProvider
-
- get(Param<T>) - 类 中的方法org.apache.spark.ml.param.ParamMap
-
Optionally returns the value associated with a param.
- get(Param<T>) - 接口 中的方法org.apache.spark.ml.param.Params
-
Optionally returns the user-supplied value of a param.
- get(String) - 类 中的方法org.apache.spark.SparkConf
-
Get a parameter; throws a NoSuchElementException if it's not set
- get(String, String) - 类 中的方法org.apache.spark.SparkConf
-
Get a parameter, falling back to a default if not set
- get() - 类 中的静态方法org.apache.spark.SparkEnv
-
Returns the SparkEnv.
- get(String) - 类 中的静态方法org.apache.spark.SparkFiles
-
Get the absolute path of a file added through SparkContext.addFile().
- get() - 接口 中的方法org.apache.spark.sql.connector.read.PartitionReader
-
Return the current record.
- get(String) - 类 中的静态方法org.apache.spark.sql.jdbc.JdbcDialects
-
Fetch the JdbcDialect class corresponding to a given database url.
- get(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i.
- get(String) - 类 中的方法org.apache.spark.sql.RuntimeConfig
-
Returns the value of Spark runtime configuration property for the given key.
- get(String, String) - 类 中的方法org.apache.spark.sql.RuntimeConfig
-
Returns the value of Spark runtime configuration property for the given key.
- get() - 接口 中的方法org.apache.spark.sql.streaming.GroupState
-
Get the state value if it exists, or throw NoSuchElementException.
- get(UUID) - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryManager
-
Returns the query if there is an active query with the given id, or null.
- get(String) - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryManager
-
Returns the query if there is an active query with the given id, or null.
- get(Object) - 类 中的方法org.apache.spark.sql.util.CaseInsensitiveStringMap
-
- get(int, DataType) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- get(int, DataType) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- get() - 类 中的方法org.apache.spark.streaming.State
-
Get the state if it exists, otherwise it will throw java.util.NoSuchElementException.
- get() - 类 中的静态方法org.apache.spark.TaskContext
-
Return the currently active TaskContext.
- get(long) - 类 中的静态方法org.apache.spark.util.AccumulatorContext
-
- get_json_object(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Extracts json object from a json string based on json path specified, and returns json string
of the extracted json object.
- getAcceptanceResults(RDD<Tuple2<K, V>>, boolean, Map<K, Object>, Option<Map<K, Object>>, long) - 类 中的静态方法org.apache.spark.util.random.StratifiedSamplingUtils
-
Count the number of items instantly accepted and generate the waitlist for each stratum.
- getActive() - 类 中的静态方法org.apache.spark.streaming.StreamingContext
-
Get the currently active context, if there is one.
- getActiveJobIds() - 类 中的方法org.apache.spark.api.java.JavaSparkStatusTracker
-
Returns an array containing the ids of all active jobs.
- getActiveJobIds() - 类 中的方法org.apache.spark.SparkStatusTracker
-
Returns an array containing the ids of all active jobs.
- getActiveOrCreate(Function0<StreamingContext>) - 类 中的静态方法org.apache.spark.streaming.StreamingContext
-
Either return the "active" StreamingContext (that is, started but not stopped), or create a
new StreamingContext that is
- getActiveOrCreate(String, Function0<StreamingContext>, Configuration, boolean) - 类 中的静态方法org.apache.spark.streaming.StreamingContext
-
Either get the currently active StreamingContext (that is, started but not stopped),
OR recreate a StreamingContext from checkpoint data in the given path.
- getActiveSession() - 类 中的静态方法org.apache.spark.sql.SparkSession
-
Returns the active SparkSession for the current thread, returned by the builder.
- getActiveStageIds() - 类 中的方法org.apache.spark.api.java.JavaSparkStatusTracker
-
Returns an array containing the ids of all active stages.
- getActiveStageIds() - 类 中的方法org.apache.spark.SparkStatusTracker
-
Returns an array containing the ids of all active stages.
- getAggregationDepth() - 接口 中的方法org.apache.spark.ml.param.shared.HasAggregationDepth
-
- getAlgo() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- getAll() - 类 中的方法org.apache.spark.SparkConf
-
Get all parameters as a list of pairs
- getAll() - 类 中的方法org.apache.spark.sql.RuntimeConfig
-
Returns all properties set in this conf.
- getAllClusterConfigs(SparkConf) - 类 中的静态方法org.apache.spark.kafka010.KafkaTokenSparkConf
-
- getAllConfs() - 类 中的方法org.apache.spark.sql.SQLContext
-
Return all the configuration properties that have been set (i.e. not the default).
- getAllPools() - 类 中的方法org.apache.spark.SparkContext
-
:: DeveloperApi ::
Return pools for fair scheduler
- GetAllReceiverInfo - org.apache.spark.streaming.scheduler中的类
-
- GetAllReceiverInfo() - 类 的构造器org.apache.spark.streaming.scheduler.GetAllReceiverInfo
-
- getAllWithPrefix(String) - 类 中的方法org.apache.spark.SparkConf
-
Get all parameters that start with prefix
- getAlpha() - 接口 中的方法org.apache.spark.ml.recommendation.ALSParams
-
- getAlpha() - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Alias for getDocConcentration
- getAnyValAs(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i.
- getAppId() - 接口 中的方法org.apache.spark.launcher.SparkAppHandle
-
Returns the application ID, or null if not yet known.
- getAppId() - 类 中的方法org.apache.spark.SparkConf
-
Returns the Spark application id, valid in the Driver after TaskScheduler registration and
from the start in the Executor.
- getApplicationInfo(String) - 接口 中的方法org.apache.spark.status.api.v1.UIRoot
-
- getApplicationInfoList() - 接口 中的方法org.apache.spark.status.api.v1.UIRoot
-
- getArray(int) - 类 中的方法org.apache.spark.sql.vectorized.ArrowColumnVector
-
- getArray(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- getArray(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- getArray(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Returns the array type value for rowId.
- getAs(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i.
- getAs(String) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value of a given fieldName.
- getAssociationRulesFromFP(Dataset<?>, String, String, double, Map<T, Object>, ClassTag<T>) - 类 中的静态方法org.apache.spark.ml.fpm.AssociationRules
-
Computes the association rules with confidence above minConfidence.
- getAsymmetricAlpha() - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Alias for getAsymmetricDocConcentration
- getAsymmetricDocConcentration() - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Concentration parameter (commonly named "alpha") for the prior placed on documents'
distributions over topics ("theta").
- getAttr(String) - 类 中的方法org.apache.spark.ml.attribute.AttributeGroup
-
Gets an attribute by its name.
- getAttr(int) - 类 中的方法org.apache.spark.ml.attribute.AttributeGroup
-
Gets an attribute by its index.
- getAvroSchema() - 类 中的方法org.apache.spark.SparkConf
-
Gets all the avro schemas in the configuration used in the generic Avro record serializer
- getBatchingTimeout(SparkConf) - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
How long we will wait for the wrappedLog in the BatchedWriteAheadLog to write the records
before we fail the write attempt to unblock receivers.
- getBernoulliSamplingFunction(RDD<Tuple2<K, V>>, Map<K, Object>, boolean, long) - 类 中的静态方法org.apache.spark.util.random.StratifiedSamplingUtils
-
Return the per partition sampling function used for sampling without replacement.
- getBeta() - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- getBeta() - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Alias for getTopicConcentration
- getBinary() - 接口 中的方法org.apache.spark.ml.feature.CountVectorizerParams
-
- getBinary() - 类 中的方法org.apache.spark.ml.feature.HashingTF
-
- getBinary(int) - 类 中的方法org.apache.spark.sql.vectorized.ArrowColumnVector
-
- getBinary(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- getBinary(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- getBinary(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Returns the binary type value for rowId.
- getBinaryWritable(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getBinaryWritableConstantObjectInspector(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getBlockSize() - 接口 中的方法org.apache.spark.ml.classification.MultilayerPerceptronParams
-
- GetBlockStatus(BlockId, boolean) - 类 的构造器org.apache.spark.storage.BlockManagerMessages.GetBlockStatus
-
- GetBlockStatus$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.GetBlockStatus$
-
- getBoolean(String, boolean) - 类 中的方法org.apache.spark.SparkConf
-
Get a parameter as a boolean, falling back to a default if not set
- getBoolean(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i as a primitive boolean.
- getBoolean(String) - 类 中的方法org.apache.spark.sql.types.Metadata
-
Gets a Boolean.
- getBoolean(String, boolean) - 类 中的方法org.apache.spark.sql.util.CaseInsensitiveStringMap
-
Returns the boolean value to which the specified key is mapped,
or defaultValue if there is no mapping for the key.
- getBoolean(int) - 类 中的方法org.apache.spark.sql.vectorized.ArrowColumnVector
-
- getBoolean(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- getBoolean(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- getBoolean(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Returns the boolean type value for rowId.
- getBooleanArray(String) - 类 中的方法org.apache.spark.sql.types.Metadata
-
Gets a Boolean array.
- getBooleans(int, int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Gets boolean type values from [rowId, rowId + count).
- getBooleanWritable(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getBooleanWritableConstantObjectInspector(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getBucketLength() - 接口 中的方法org.apache.spark.ml.feature.BucketedRandomProjectionLSHParams
-
- getBuilder() - 类 中的方法org.apache.spark.storage.memory.DeserializedValuesHolder
-
- getBuilder() - 类 中的方法org.apache.spark.storage.memory.SerializedValuesHolder
-
- getBuilder() - 接口 中的方法org.apache.spark.storage.memory.ValuesHolder
-
Note: After this method is called, the ValuesHolder is invalid, we can't store data and
get estimate size again.
- getByte(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i as a primitive byte.
- getByte(int) - 类 中的方法org.apache.spark.sql.vectorized.ArrowColumnVector
-
- getByte(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- getByte(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- getByte(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Returns the byte type value for rowId.
- getBytes(int, int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Gets byte type values from [rowId, rowId + count).
- getByteWritable(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getByteWritableConstantObjectInspector(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getCachedBlockManagerId(BlockManagerId) - 类 中的静态方法org.apache.spark.storage.BlockManagerId
-
- getCachedMetadata(String) - 类 中的静态方法org.apache.spark.rdd.HadoopRDD
-
The three methods below are helpers for accessing the local map, a property of the SparkEnv of
the local process.
- getCacheNodeIds() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
- getCallSite(Function1<String, Object>) - 类 中的静态方法org.apache.spark.util.Utils
-
When called inside a class in the spark package, returns the name of the user code class
(outside the spark package) that called into Spark, as well as which Spark method they called.
- getCaseSensitive() - 类 中的方法org.apache.spark.ml.feature.StopWordsRemover
-
- getCatalystType(int, String, int, MetadataBuilder) - 类 中的方法org.apache.spark.sql.jdbc.AggregatedDialect
-
- getCatalystType(int, String, int, MetadataBuilder) - 类 中的静态方法org.apache.spark.sql.jdbc.DB2Dialect
-
- getCatalystType(int, String, int, MetadataBuilder) - 类 中的静态方法org.apache.spark.sql.jdbc.DerbyDialect
-
- getCatalystType(int, String, int, MetadataBuilder) - 类 中的方法org.apache.spark.sql.jdbc.JdbcDialect
-
Get the custom datatype mapping for the given jdbc meta information.
- getCatalystType(int, String, int, MetadataBuilder) - 类 中的静态方法org.apache.spark.sql.jdbc.MsSqlServerDialect
-
- getCatalystType(int, String, int, MetadataBuilder) - 类 中的静态方法org.apache.spark.sql.jdbc.MySQLDialect
-
- getCatalystType(int, String, int, MetadataBuilder) - 类 中的静态方法org.apache.spark.sql.jdbc.NoopDialect
-
- getCatalystType(int, String, int, MetadataBuilder) - 类 中的静态方法org.apache.spark.sql.jdbc.OracleDialect
-
- getCatalystType(int, String, int, MetadataBuilder) - 类 中的静态方法org.apache.spark.sql.jdbc.PostgresDialect
-
- getCatalystType(int, String, int, MetadataBuilder) - 类 中的静态方法org.apache.spark.sql.jdbc.TeradataDialect
-
- getCategoricalCols() - 类 中的方法org.apache.spark.ml.feature.FeatureHasher
-
- getCategoricalFeatures(StructField) - 类 中的静态方法org.apache.spark.ml.util.MetadataUtils
-
Examine a schema to identify categorical (Binary and Nominal) features.
- getCategoricalFeaturesInfo() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- getCensorCol() - 接口 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionParams
-
- getCheckpointDir() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
- getCheckpointDir() - 类 中的方法org.apache.spark.SparkContext
-
- getCheckpointFile() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Gets the name of the file to which this RDD was checkpointed
- getCheckpointFile() - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
- getCheckpointFile() - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- getCheckpointFile() - 类 中的方法org.apache.spark.rdd.RDD
-
Gets the name of the directory to which this RDD was checkpointed.
- getCheckpointFiles() - 类 中的方法org.apache.spark.graphx.Graph
-
Gets the name of the files to which this Graph was checkpointed.
- getCheckpointFiles() - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- getCheckpointFiles() - 类 中的方法org.apache.spark.ml.clustering.DistributedLDAModel
-
:: DeveloperApi ::
If using checkpointing and LDA.keepLastCheckpoint is set to true, then there may be
saved checkpoint files.
- getCheckpointInterval() - 接口 中的方法org.apache.spark.ml.param.shared.HasCheckpointInterval
-
- getCheckpointInterval() - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Period (in iterations) between checkpoints.
- getCheckpointInterval() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- getChild(int) - 类 中的方法org.apache.spark.sql.vectorized.ArrowColumnVector
-
- getChild(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
- getClassifier() - 接口 中的方法org.apache.spark.ml.classification.OneVsRestParams
-
- getClusterConfig(SparkConf, String) - 类 中的静态方法org.apache.spark.kafka010.KafkaTokenSparkConf
-
- getColdStartStrategy() - 接口 中的方法org.apache.spark.ml.recommendation.ALSModelParams
-
- getCollectSubModels() - 接口 中的方法org.apache.spark.ml.param.shared.HasCollectSubModels
-
- getColumnName(Seq<Object>, StructType) - 类 中的静态方法org.apache.spark.sql.util.SchemaUtils
-
Gets the name of the column in the given position.
- getCombOp() - 类 中的静态方法org.apache.spark.util.random.StratifiedSamplingUtils
-
Returns the function used combine results returned by seqOp from different partitions.
- getComment() - 类 中的方法org.apache.spark.sql.types.StructField
-
Return the comment of this StructField.
- getConf() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Return a copy of this JavaSparkContext's configuration.
- getConf() - 接口 中的方法org.apache.spark.input.Configurable
-
- getConf() - 类 中的方法org.apache.spark.rdd.HadoopRDD
-
- getConf() - 类 中的方法org.apache.spark.rdd.NewHadoopRDD
-
- getConf() - 类 中的方法org.apache.spark.SparkContext
-
Return a copy of this SparkContext's configuration.
- getConf(String, String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Returns the configuration for the given key in the current session.
- getConf(String) - 类 中的方法org.apache.spark.sql.SQLContext
-
Return the value of Spark SQL configuration property for the given key.
- getConf(String, String) - 类 中的方法org.apache.spark.sql.SQLContext
-
Return the value of Spark SQL configuration property for the given key.
- getConfiguration() - 类 中的方法org.apache.spark.input.PortableDataStream
-
- getConfiguredLocalDirs(SparkConf) - 类 中的静态方法org.apache.spark.util.Utils
-
Return the configured local directories where Spark can write files.
- getConnection() - 接口 中的方法org.apache.spark.rdd.JdbcRDD.ConnectionFactory
-
- getContextOrSparkClassLoader() - 类 中的静态方法org.apache.spark.util.Utils
-
Get the Context ClassLoader on this thread or, if not present, the ClassLoader that
loaded Spark.
- getConvergenceTol() - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixture
-
Return the largest change in log-likelihood at which convergence is
considered to have occurred.
- getCorrelationFromName(String) - 类 中的静态方法org.apache.spark.mllib.stat.correlation.Correlations
-
- getCount() - 类 中的方法org.apache.spark.storage.CountingWritableChannel
-
- getCurrentProcessingTimeMs() - 接口 中的方法org.apache.spark.sql.streaming.GroupState
-
Get the current processing time as milliseconds in epoch time.
- getCurrentUserGroups(SparkConf, String) - 类 中的静态方法org.apache.spark.util.Utils
-
- getCurrentUserName() - 类 中的静态方法org.apache.spark.util.Utils
-
Returns the current user name.
- getCurrentWatermarkMs() - 接口 中的方法org.apache.spark.sql.streaming.GroupState
-
Get the current event time watermark as milliseconds in epoch time.
- getData(Row) - 类 中的静态方法org.apache.spark.ml.image.ImageSchema
-
Gets the image data
- getDatabase(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Get the database with the specified name.
- getDatabase(String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Returns the metadata for specified database, throwing an exception if it doesn't exist
- getDate(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i of date type as java.sql.Date.
- getDateWritable(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getDateWritableConstantObjectInspector(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getDecimal(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i of decimal type as java.math.BigDecimal.
- getDecimal(int, int, int) - 类 中的方法org.apache.spark.sql.vectorized.ArrowColumnVector
-
- getDecimal(int, int, int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- getDecimal(int, int, int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- getDecimal(int, int, int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Returns the decimal type value for rowId.
- getDecimalWritable(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getDecimalWritableConstantObjectInspector(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getDefault(Param<T>) - 接口 中的方法org.apache.spark.ml.param.Params
-
Gets the default value of a parameter.
- getDefaultPropertiesFile(Map<String, String>) - 类 中的静态方法org.apache.spark.util.Utils
-
Return the path of the default Spark properties file.
- getDefaultSession() - 类 中的静态方法org.apache.spark.sql.SparkSession
-
Returns the default SparkSession that is returned by the builder.
- getDegree() - 类 中的方法org.apache.spark.ml.feature.PolynomialExpansion
-
- getDenseSizeInBytes() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Gets the size of the dense representation of this `Matrix`.
- getDependencies() - 类 中的方法org.apache.spark.rdd.CoGroupedRDD
-
- getDependencies() - 类 中的方法org.apache.spark.rdd.ShuffledRDD
-
- getDependencies() - 类 中的方法org.apache.spark.rdd.UnionRDD
-
- getDeprecatedConfig(String, Map<String, String>) - 类 中的静态方法org.apache.spark.SparkConf
-
Looks for available deprecated keys for the given config option, and return the first
value available.
- getDistanceMeasure() - 类 中的方法org.apache.spark.ml.evaluation.ClusteringEvaluator
-
- getDistanceMeasure() - 接口 中的方法org.apache.spark.ml.param.shared.HasDistanceMeasure
-
- getDistanceMeasure() - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeans
-
The distance suite used by the algorithm.
- getDistanceMeasure() - 类 中的方法org.apache.spark.mllib.clustering.KMeans
-
The distance suite used by the algorithm.
- getDistributions() - 类 中的方法org.apache.spark.status.LiveRDD
-
- getDocConcentration() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
- getDocConcentration() - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Concentration parameter (commonly named "alpha") for the prior placed on documents'
distributions over topics ("theta").
- getDouble(String, double) - 类 中的方法org.apache.spark.SparkConf
-
Get a parameter as a double, falling back to a default if not ste
- getDouble(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i as a primitive double.
- getDouble(String) - 类 中的方法org.apache.spark.sql.types.Metadata
-
Gets a Double.
- getDouble(String, double) - 类 中的方法org.apache.spark.sql.util.CaseInsensitiveStringMap
-
Returns the double value to which the specified key is mapped,
or defaultValue if there is no mapping for the key.
- getDouble(int) - 类 中的方法org.apache.spark.sql.vectorized.ArrowColumnVector
-
- getDouble(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- getDouble(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- getDouble(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Returns the double type value for rowId.
- getDoubleArray(String) - 类 中的方法org.apache.spark.sql.types.Metadata
-
Gets a Double array.
- getDoubles(int, int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Gets double type values from [rowId, rowId + count).
- getDoubleWritable(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getDoubleWritableConstantObjectInspector(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getDriverAttributes() - 接口 中的方法org.apache.spark.scheduler.SchedulerBackend
-
Get the attributes on driver.
- getDriverLogUrls() - 接口 中的方法org.apache.spark.scheduler.SchedulerBackend
-
Get the URLs for the driver logs.
- getDropLast() - 接口 中的方法org.apache.spark.ml.feature.OneHotEncoderBase
-
- getDstCol() - 接口 中的方法org.apache.spark.ml.clustering.PowerIterationClusteringParams
-
- getDynamicAllocationInitialExecutors(SparkConf) - 类 中的静态方法org.apache.spark.util.Utils
-
Return the initial number of executors for dynamic allocation.
- getElasticNetParam() - 接口 中的方法org.apache.spark.ml.param.shared.HasElasticNetParam
-
- getEndTimeEpoch() - 类 中的方法org.apache.spark.status.api.v1.ApplicationAttemptInfo
-
- getEps() - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- getEpsilon() - 接口 中的方法org.apache.spark.ml.regression.LinearRegressionParams
-
- getEpsilon() - 类 中的方法org.apache.spark.mllib.clustering.KMeans
-
The distance threshold within which we've consider centers to have converged.
- getError() - 接口 中的方法org.apache.spark.launcher.SparkAppHandle
-
If the application failed due to an error, return the underlying error.
- getEstimator() - 接口 中的方法org.apache.spark.ml.tuning.ValidatorParams
-
- getEstimatorParamMaps() - 接口 中的方法org.apache.spark.ml.tuning.ValidatorParams
-
- getEvaluator() - 接口 中的方法org.apache.spark.ml.tuning.ValidatorParams
-
- getExecutionContext() - 接口 中的方法org.apache.spark.ml.param.shared.HasParallelism
-
Create a new execution context with a thread-pool that has a maximum number of threads
set to the value of parallelism.
- GetExecutorEndpointRef(String) - 类 的构造器org.apache.spark.storage.BlockManagerMessages.GetExecutorEndpointRef
-
- GetExecutorEndpointRef$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.GetExecutorEndpointRef$
-
- getExecutorEnv() - 类 中的方法org.apache.spark.SparkConf
-
Get all executor environment variables set on this SparkConf
- getExecutorIds() - 接口 中的方法org.apache.spark.ExecutorAllocationClient
-
Get the list of currently active executors
- getExecutorInfos() - 类 中的方法org.apache.spark.SparkStatusTracker
-
Returns information of all known executors, including host, port, cacheSize, numRunningTasks
and memory metrics.
- GetExecutorLossReason(String) - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.GetExecutorLossReason
-
- GetExecutorLossReason$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.GetExecutorLossReason$
-
- getExecutorMemoryStatus() - 类 中的方法org.apache.spark.SparkContext
-
Return a map from the slave to the max memory available for caching and the remaining
memory available for caching.
- getExternalScratchDir(URI, Configuration, String) - 接口 中的方法org.apache.spark.sql.hive.execution.SaveAsHiveFile
-
- getExternalTmpPath(SparkSession, Configuration, Path) - 接口 中的方法org.apache.spark.sql.hive.execution.SaveAsHiveFile
-
- getExtTmpPathRelTo(Path, Configuration, String) - 接口 中的方法org.apache.spark.sql.hive.execution.SaveAsHiveFile
-
- getFamily() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionParams
-
- getFamily() - 接口 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionBase
-
- getFdr() - 接口 中的方法org.apache.spark.ml.feature.ChiSqSelectorParams
-
- getFeatureIndex() - 接口 中的方法org.apache.spark.ml.regression.IsotonicRegressionBase
-
- getFeatureIndicesFromNames(StructField, String[]) - 类 中的静态方法org.apache.spark.ml.util.MetadataUtils
-
Takes a Vector column and a list of feature names, and returns the corresponding list of
feature indices in the column, in order.
- getFeatures() - 类 中的方法org.apache.spark.ml.feature.LabeledPoint
-
- getFeatures() - 类 中的方法org.apache.spark.mllib.regression.LabeledPoint
-
- getFeaturesAndLabels(RFormulaModel, Dataset<?>) - 类 中的静态方法org.apache.spark.ml.r.RWrapperUtils
-
Get the feature names and original labels from the schema
of DataFrame transformed by RFormulaModel.
- getFeaturesCol() - 接口 中的方法org.apache.spark.ml.param.shared.HasFeaturesCol
-
- getFeatureSubsetStrategy() - 接口 中的方法org.apache.spark.ml.tree.TreeEnsembleParams
-
- getField(String) - 类 中的方法org.apache.spark.sql.Column
-
An expression that gets a field by name in a StructType.
- getFileLength(File, SparkConf) - 类 中的静态方法org.apache.spark.util.Utils
-
Return the file length, if the file is compressed it returns the uncompressed file length.
- getFileReader(String, Option<Configuration>, boolean) - 类 中的静态方法org.apache.spark.sql.hive.orc.OrcFileOperator
-
Retrieves an ORC file reader from a given path.
- getFileSegmentLocations(String, long, long, Configuration) - 类 中的静态方法org.apache.spark.streaming.util.HdfsUtils
-
Get the locations of the HDFS blocks containing the given file segment.
- getFileSystemForPath(Path, Configuration) - 类 中的静态方法org.apache.spark.streaming.util.HdfsUtils
-
- getFinalStorageLevel() - 接口 中的方法org.apache.spark.ml.recommendation.ALSParams
-
- getFinalValue() - 类 中的方法org.apache.spark.partial.PartialResult
-
Blocking method to wait for and return the final value.
- getFitIntercept() - 接口 中的方法org.apache.spark.ml.param.shared.HasFitIntercept
-
- getFloat(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i as a primitive float.
- getFloat(int) - 类 中的方法org.apache.spark.sql.vectorized.ArrowColumnVector
-
- getFloat(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- getFloat(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- getFloat(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Returns the float type value for rowId.
- getFloats(int, int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Gets float type values from [rowId, rowId + count).
- getFloatWritable(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getFloatWritableConstantObjectInspector(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getForceIndexLabel() - 接口 中的方法org.apache.spark.ml.feature.RFormulaBase
-
- getFormattedClassName(Object) - 类 中的静态方法org.apache.spark.util.Utils
-
Return the class name of the given object, removing all dollar signs
- getFormula() - 接口 中的方法org.apache.spark.ml.feature.RFormulaBase
-
- getFpr() - 接口 中的方法org.apache.spark.ml.feature.ChiSqSelectorParams
-
- getFunction(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Get the function with the specified name.
- getFunction(String, String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Get the function with the specified name.
- getFunction(String, String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Return an existing function in the database, assuming it exists.
- getFunctionOption(String, String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Return an existing function in the database, or None if it doesn't exist.
- getFwe() - 接口 中的方法org.apache.spark.ml.feature.ChiSqSelectorParams
-
- getGaps() - 类 中的方法org.apache.spark.ml.feature.RegexTokenizer
-
- getGroups(String) - 接口 中的方法org.apache.spark.security.GroupMappingServiceProvider
-
Get the groups the user belongs to.
- getHadoopFileSystem(URI, Configuration) - 类 中的静态方法org.apache.spark.util.Utils
-
Return a Hadoop FileSystem with the scheme encoded in the given path.
- getHadoopFileSystem(String, Configuration) - 类 中的静态方法org.apache.spark.util.Utils
-
Return a Hadoop FileSystem with the scheme encoded in the given path.
- getHandleInvalid() - 接口 中的方法org.apache.spark.ml.param.shared.HasHandleInvalid
-
- getHeight(Row) - 类 中的静态方法org.apache.spark.ml.image.ImageSchema
-
Gets the height of the image
- getHiveWriteCompression(TableDesc, SQLConf) - 类 中的静态方法org.apache.spark.sql.hive.execution.HiveOptions
-
- getImplicitPrefs() - 接口 中的方法org.apache.spark.ml.recommendation.ALSParams
-
- getImpurity() - 接口 中的方法org.apache.spark.ml.tree.HasVarianceImpurity
-
- getImpurity() - 接口 中的方法org.apache.spark.ml.tree.TreeClassifierParams
-
- getImpurity() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- getIndices() - 类 中的方法org.apache.spark.ml.feature.VectorSlicer
-
- getInitializationMode() - 类 中的方法org.apache.spark.mllib.clustering.KMeans
-
The initialization algorithm.
- getInitializationSteps() - 类 中的方法org.apache.spark.mllib.clustering.KMeans
-
Number of steps for the k-means|| initialization mode
- getInitialModel() - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixture
-
Return the user supplied initial GMM, if supplied
- getInitialTargetExecutorNumber(SparkConf, int) - 类 中的静态方法org.apache.spark.scheduler.cluster.SchedulerBackendUtils
-
Getting the initial target number of executors depends on whether dynamic allocation is
enabled.
- getInitialWeights() - 接口 中的方法org.apache.spark.ml.classification.MultilayerPerceptronParams
-
- getInitMode() - 接口 中的方法org.apache.spark.ml.clustering.KMeansParams
-
- getInitMode() - 接口 中的方法org.apache.spark.ml.clustering.PowerIterationClusteringParams
-
- getInitSteps() - 接口 中的方法org.apache.spark.ml.clustering.KMeansParams
-
- getInOutCols() - 接口 中的方法org.apache.spark.ml.feature.ImputerParams
-
Returns the input and output column names corresponding in pair.
- getInOutCols() - 接口 中的方法org.apache.spark.ml.feature.OneHotEncoderBase
-
Returns the input and output column names corresponding in pair.
- getInOutCols() - 接口 中的方法org.apache.spark.ml.feature.StringIndexerBase
-
Returns the input and output column names corresponding in pair.
- getInputCol() - 接口 中的方法org.apache.spark.ml.param.shared.HasInputCol
-
- getInputCols() - 接口 中的方法org.apache.spark.ml.param.shared.HasInputCols
-
- getInputFilePath() - 类 中的静态方法org.apache.spark.rdd.InputFileBlockHolder
-
Returns the holding file name or empty string if it is unknown.
- getInputStream(String, Configuration) - 类 中的静态方法org.apache.spark.streaming.util.HdfsUtils
-
- getInstant(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i of date type as java.time.Instant.
- getInt(String, int) - 类 中的方法org.apache.spark.SparkConf
-
Get a parameter as an integer, falling back to a default if not set
- getInt(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i as a primitive int.
- getInt(String, int) - 类 中的方法org.apache.spark.sql.util.CaseInsensitiveStringMap
-
Returns the integer value to which the specified key is mapped,
or defaultValue if there is no mapping for the key.
- getInt(int) - 类 中的方法org.apache.spark.sql.vectorized.ArrowColumnVector
-
- getInt(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- getInt(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- getInt(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Returns the int type value for rowId.
- getIntermediateStorageLevel() - 接口 中的方法org.apache.spark.ml.recommendation.ALSParams
-
- getInterval(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- getInterval(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- getInterval(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Returns the calendar interval type value for rowId.
- getInts(int, int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Gets int type values from [rowId, rowId + count).
- getIntWritable(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getIntWritableConstantObjectInspector(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getInverse() - 类 中的方法org.apache.spark.ml.feature.DCT
-
- getIsExperiment() - 类 中的方法org.apache.spark.mllib.stat.test.BinarySample
-
- getIsotonic() - 接口 中的方法org.apache.spark.ml.regression.IsotonicRegressionBase
-
- getItem(Object) - 类 中的方法org.apache.spark.sql.Column
-
An expression that gets an item at position ordinal out of an array,
or gets a value by key key in a MapType.
- getItemCol() - 接口 中的方法org.apache.spark.ml.recommendation.ALSModelParams
-
- getItemsCol() - 接口 中的方法org.apache.spark.ml.fpm.FPGrowthParams
-
- getIteratorSize(Iterator<?>) - 类 中的静态方法org.apache.spark.util.Utils
-
Counts the number of elements of an iterator using a while loop rather than calling
TraversableOnce.size() because it uses a for loop, which is slightly slower
in the current version of Scala.
- getIteratorZipWithIndex(Iterator<T>, long) - 类 中的静态方法org.apache.spark.util.Utils
-
Generate a zipWithIndex iterator, avoid index value overflowing problem
in scala's zipWithIndex
- getJavaMap(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i of array type as a java.util.Map.
- getJavaSparkContext(SparkSession) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
- getJDBCType(DataType) - 类 中的方法org.apache.spark.sql.jdbc.AggregatedDialect
-
- getJDBCType(DataType) - 类 中的静态方法org.apache.spark.sql.jdbc.DB2Dialect
-
- getJDBCType(DataType) - 类 中的静态方法org.apache.spark.sql.jdbc.DerbyDialect
-
- getJDBCType(DataType) - 类 中的方法org.apache.spark.sql.jdbc.JdbcDialect
-
Retrieve the jdbc / sql type for a given datatype.
- getJDBCType(DataType) - 类 中的静态方法org.apache.spark.sql.jdbc.MsSqlServerDialect
-
- getJDBCType(DataType) - 类 中的静态方法org.apache.spark.sql.jdbc.MySQLDialect
-
- getJDBCType(DataType) - 类 中的静态方法org.apache.spark.sql.jdbc.NoopDialect
-
- getJDBCType(DataType) - 类 中的静态方法org.apache.spark.sql.jdbc.OracleDialect
-
- getJDBCType(DataType) - 类 中的静态方法org.apache.spark.sql.jdbc.PostgresDialect
-
- getJDBCType(DataType) - 类 中的静态方法org.apache.spark.sql.jdbc.TeradataDialect
-
- getJobIdsForGroup(String) - 类 中的方法org.apache.spark.api.java.JavaSparkStatusTracker
-
Return a list of all known jobs in a particular job group.
- getJobIdsForGroup(String) - 类 中的方法org.apache.spark.SparkStatusTracker
-
Return a list of all known jobs in a particular job group.
- getJobInfo(int) - 类 中的方法org.apache.spark.api.java.JavaSparkStatusTracker
-
Returns job information, or null if the job info could not be found or was garbage collected.
- getJobInfo(int) - 类 中的方法org.apache.spark.SparkStatusTracker
-
Returns job information, or None if the job info could not be found or was garbage collected.
- getK() - 接口 中的方法org.apache.spark.ml.clustering.BisectingKMeansParams
-
- getK() - 接口 中的方法org.apache.spark.ml.clustering.GaussianMixtureParams
-
- getK() - 接口 中的方法org.apache.spark.ml.clustering.KMeansParams
-
- getK() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
- getK() - 接口 中的方法org.apache.spark.ml.clustering.PowerIterationClusteringParams
-
- getK() - 类 中的方法org.apache.spark.ml.evaluation.RankingEvaluator
-
- getK() - 接口 中的方法org.apache.spark.ml.feature.PCAParams
-
- getK() - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeans
-
Gets the desired number of leaf clusters.
- getK() - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixture
-
Return the number of Gaussians in the mixture model
- getK() - 类 中的方法org.apache.spark.mllib.clustering.KMeans
-
Number of clusters to create (k).
- getK() - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Number of topics to infer, i.e., the number of soft cluster centers.
- getKappa() - 类 中的方法org.apache.spark.mllib.clustering.OnlineLDAOptimizer
-
Learning rate: exponential decay rate
- getKeepLastCheckpoint() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
- getKeepLastCheckpoint() - 类 中的方法org.apache.spark.mllib.clustering.EMLDAOptimizer
-
If using checkpointing, this indicates whether to keep the last checkpoint (vs clean up).
- getKeytabJaasParams(String, String, String) - 类 中的静态方法org.apache.spark.kafka010.KafkaTokenUtil
-
- getKrb5LoginModuleName() - 类 中的静态方法org.apache.spark.kafka010.KafkaTokenUtil
-
Krb5LoginModule package vary in different JVMs.
- getLabel() - 类 中的方法org.apache.spark.ml.feature.LabeledPoint
-
- getLabel() - 类 中的方法org.apache.spark.mllib.regression.LabeledPoint
-
- getLabelCol() - 接口 中的方法org.apache.spark.ml.param.shared.HasLabelCol
-
- getLabels() - 类 中的方法org.apache.spark.ml.feature.IndexToString
-
- getLambda() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayes
-
Get the smoothing parameter.
- getLastUpdatedEpoch() - 类 中的方法org.apache.spark.status.api.v1.ApplicationAttemptInfo
-
- getLayers() - 接口 中的方法org.apache.spark.ml.classification.MultilayerPerceptronParams
-
- getLDAModel(double[]) - 接口 中的方法org.apache.spark.mllib.clustering.LDAOptimizer
-
- getLeafCol() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
- getLearningDecay() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
- getLearningOffset() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
- getLearningRate() - 类 中的方法org.apache.spark.mllib.tree.configuration.BoostingStrategy
-
- getLeastGroupHash(String) - 类 中的方法org.apache.spark.rdd.DefaultPartitionCoalescer
-
Gets the least element of the list associated with key in groupHash
The returned PartitionGroup is the least loaded of all groups that represent the machine "key"
- getLength() - 类 中的静态方法org.apache.spark.rdd.InputFileBlockHolder
-
Returns the length of the block being read, or -1 if it is unknown.
- getLink() - 接口 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionBase
-
- getLinkPower() - 接口 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionBase
-
- getLinkPredictionCol() - 接口 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionBase
-
- getList(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i of array type as java.util.List.
- getLocalDate(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i of date type as java.time.LocalDate.
- getLocalDir(SparkConf) - 类 中的静态方法org.apache.spark.util.Utils
-
Get the path of a temporary directory.
- getLocale() - 类 中的方法org.apache.spark.ml.feature.StopWordsRemover
-
- getLocalProperty(String) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Get a local property set in this thread, or null if it is missing.
- getLocalProperty(String) - 类 中的方法org.apache.spark.BarrierTaskContext
-
- getLocalProperty(String) - 类 中的方法org.apache.spark.SparkContext
-
Get a local property set in this thread, or null if it is missing.
- getLocalProperty(String) - 类 中的方法org.apache.spark.TaskContext
-
Get a local property set upstream in the driver, or null if it is missing.
- getLocalUserJarsForShell(SparkConf) - 类 中的静态方法org.apache.spark.util.Utils
-
Return the local jar files which will be added to REPL's classpath.
- GetLocations(BlockId) - 类 的构造器org.apache.spark.storage.BlockManagerMessages.GetLocations
-
- GetLocations$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.GetLocations$
-
- GetLocationsAndStatus(BlockId, String) - 类 的构造器org.apache.spark.storage.BlockManagerMessages.GetLocationsAndStatus
-
- GetLocationsAndStatus$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.GetLocationsAndStatus$
-
- GetLocationsMultipleBlockIds(BlockId[]) - 类 的构造器org.apache.spark.storage.BlockManagerMessages.GetLocationsMultipleBlockIds
-
- GetLocationsMultipleBlockIds$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.GetLocationsMultipleBlockIds$
-
- getLong(String, long) - 类 中的方法org.apache.spark.SparkConf
-
Get a parameter as a long, falling back to a default if not set
- getLong(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i as a primitive long.
- getLong(String) - 类 中的方法org.apache.spark.sql.types.Metadata
-
Gets a Long.
- getLong(String, long) - 类 中的方法org.apache.spark.sql.util.CaseInsensitiveStringMap
-
Returns the long value to which the specified key is mapped,
or defaultValue if there is no mapping for the key.
- getLong(int) - 类 中的方法org.apache.spark.sql.vectorized.ArrowColumnVector
-
- getLong(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- getLong(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- getLong(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Returns the long type value for rowId.
- getLongArray(String) - 类 中的方法org.apache.spark.sql.types.Metadata
-
Gets a Long array.
- getLongs(int, int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Gets long type values from [rowId, rowId + count).
- getLongWritable(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getLongWritableConstantObjectInspector(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getLoss() - 接口 中的方法org.apache.spark.ml.param.shared.HasLoss
-
- getLoss() - 类 中的方法org.apache.spark.mllib.tree.configuration.BoostingStrategy
-
- getLossType() - 接口 中的方法org.apache.spark.ml.tree.GBTClassifierParams
-
- getLossType() - 接口 中的方法org.apache.spark.ml.tree.GBTRegressorParams
-
- getLower() - 接口 中的方法org.apache.spark.ml.feature.RobustScalerParams
-
- getLowerBound(double, long, double) - 类 中的静态方法org.apache.spark.util.random.BinomialBounds
-
Returns a threshold p such that if we conduct n Bernoulli trials with success rate = p,
it is very unlikely to have more than fraction * n successes.
- getLowerBound(double) - 类 中的静态方法org.apache.spark.util.random.PoissonBounds
-
Returns a lambda such that Pr[X > s] is very small, where X ~ Pois(lambda).
- getLowerBoundsOnCoefficients() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionParams
-
- getLowerBoundsOnIntercepts() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionParams
-
- getMap(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i of map type as a Scala Map.
- getMap(int) - 类 中的方法org.apache.spark.sql.vectorized.ArrowColumnVector
-
- getMap(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- getMap(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- getMap(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Returns the map type value for rowId.
- GetMatchingBlockIds(Function1<BlockId, Object>, boolean) - 类 的构造器org.apache.spark.storage.BlockManagerMessages.GetMatchingBlockIds
-
- GetMatchingBlockIds$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.GetMatchingBlockIds$
-
- getMax() - 接口 中的方法org.apache.spark.ml.feature.MinMaxScalerParams
-
- getMaxBins() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
- getMaxBins() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- getMaxCategories() - 接口 中的方法org.apache.spark.ml.feature.VectorIndexerParams
-
- getMaxDepth() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
- getMaxDepth() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- getMaxDF() - 接口 中的方法org.apache.spark.ml.feature.CountVectorizerParams
-
- getMaxFailures(SparkConf, boolean) - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- getMaxIter() - 接口 中的方法org.apache.spark.ml.param.shared.HasMaxIter
-
- getMaxIterations() - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeans
-
Gets the max number of k-means iterations to split clusters.
- getMaxIterations() - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixture
-
Return the maximum number of iterations allowed
- getMaxIterations() - 类 中的方法org.apache.spark.mllib.clustering.KMeans
-
Maximum number of iterations allowed.
- getMaxIterations() - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Maximum number of iterations allowed.
- getMaxLocalProjDBSize() - 类 中的方法org.apache.spark.ml.fpm.PrefixSpan
-
- getMaxLocalProjDBSize() - 类 中的方法org.apache.spark.mllib.fpm.PrefixSpan
-
Gets the maximum number of items allowed in a projected database before local processing.
- getMaxMemoryInMB() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
- getMaxMemoryInMB() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- getMaxPatternLength() - 类 中的方法org.apache.spark.ml.fpm.PrefixSpan
-
- getMaxPatternLength() - 类 中的方法org.apache.spark.mllib.fpm.PrefixSpan
-
Gets the maximal pattern length (i.e. the length of the longest sequential pattern to consider.
- getMaxSentenceLength() - 接口 中的方法org.apache.spark.ml.feature.Word2VecBase
-
- GetMemoryStatus$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.GetMemoryStatus$
-
- getMessage() - 异常错误 中的方法org.apache.spark.sql.AnalysisException
-
- getMetadata(String) - 类 中的方法org.apache.spark.sql.types.Metadata
-
Gets a Metadata.
- getMetadataArray(String) - 类 中的方法org.apache.spark.sql.types.Metadata
-
Gets a Metadata array.
- getMetricLabel() - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- getMetricLabel() - 类 中的方法org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator
-
- getMetricName() - 类 中的方法org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
-
- getMetricName() - 类 中的方法org.apache.spark.ml.evaluation.ClusteringEvaluator
-
- getMetricName() - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- getMetricName() - 类 中的方法org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator
-
- getMetricName() - 类 中的方法org.apache.spark.ml.evaluation.RankingEvaluator
-
- getMetricName() - 类 中的方法org.apache.spark.ml.evaluation.RegressionEvaluator
-
- getMetricsSources(String) - 类 中的方法org.apache.spark.BarrierTaskContext
-
- getMetricsSources(String) - 类 中的方法org.apache.spark.TaskContext
-
::DeveloperApi::
Returns all metrics sources with the given name which are associated with the instance
which runs the task.
- getMetricValue(MemoryManager) - 接口 中的方法org.apache.spark.metrics.SingleValueExecutorMetricType
-
- getMetricValues(MemoryManager) - 接口 中的方法org.apache.spark.metrics.ExecutorMetricType
-
- getMetricValues(MemoryManager) - 接口 中的方法org.apache.spark.metrics.SingleValueExecutorMetricType
-
- getMin() - 接口 中的方法org.apache.spark.ml.feature.MinMaxScalerParams
-
- getMinConfidence() - 接口 中的方法org.apache.spark.ml.fpm.FPGrowthParams
-
- getMinCount() - 接口 中的方法org.apache.spark.ml.feature.Word2VecBase
-
- getMinDF() - 接口 中的方法org.apache.spark.ml.feature.CountVectorizerParams
-
- getMinDivisibleClusterSize() - 接口 中的方法org.apache.spark.ml.clustering.BisectingKMeansParams
-
- getMinDivisibleClusterSize() - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeans
-
Gets the minimum number of points (if greater than or equal to 1.0) or the minimum proportion
of points (if less than 1.0) of a divisible cluster.
- getMinDocFreq() - 接口 中的方法org.apache.spark.ml.feature.IDFBase
-
- getMiniBatchFraction() - 类 中的方法org.apache.spark.mllib.clustering.OnlineLDAOptimizer
-
Mini-batch fraction, which sets the fraction of document sampled and used in each iteration
- getMinInfoGain() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
- getMinInfoGain() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- getMinInstancesPerNode() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
- getMinInstancesPerNode() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- getMinSupport() - 接口 中的方法org.apache.spark.ml.fpm.FPGrowthParams
-
- getMinSupport() - 类 中的方法org.apache.spark.ml.fpm.PrefixSpan
-
- getMinSupport() - 类 中的方法org.apache.spark.mllib.fpm.PrefixSpan
-
Get the minimal support (i.e. the frequency of occurrence before a pattern is considered
frequent).
- getMinTF() - 接口 中的方法org.apache.spark.ml.feature.CountVectorizerParams
-
- getMinTokenLength() - 类 中的方法org.apache.spark.ml.feature.RegexTokenizer
-
- getMinWeightFractionPerNode() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
- getMinWeightFractionPerNode() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- getMissingValue() - 接口 中的方法org.apache.spark.ml.feature.ImputerParams
-
- getMode(Row) - 类 中的静态方法org.apache.spark.ml.image.ImageSchema
-
Gets the OpenCV representation as an int
- getModelType() - 接口 中的方法org.apache.spark.ml.classification.NaiveBayesParams
-
- getModelType() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayes
-
Get the model type.
- getN() - 类 中的方法org.apache.spark.ml.feature.NGram
-
- getNames() - 类 中的方法org.apache.spark.ml.feature.VectorSlicer
-
- getNChannels(Row) - 类 中的静态方法org.apache.spark.ml.image.ImageSchema
-
Gets the number of channels in the image
- getNode(int, Node) - 类 中的静态方法org.apache.spark.mllib.tree.model.Node
-
Traces down from a root node to get the node with the given node index.
- getNonnegative() - 接口 中的方法org.apache.spark.ml.recommendation.ALSParams
-
- getNumBins() - 类 中的方法org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
-
- getNumBuckets() - 接口 中的方法org.apache.spark.ml.feature.QuantileDiscretizerBase
-
- getNumBucketsArray() - 接口 中的方法org.apache.spark.ml.feature.QuantileDiscretizerBase
-
- getNumBytesWritten() - 接口 中的方法org.apache.spark.shuffle.api.ShufflePartitionWriter
-
- getNumClasses(StructField) - 类 中的静态方法org.apache.spark.ml.util.MetadataUtils
-
Examine a schema to identify the number of classes in a label column.
- getNumClasses() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- getNumFeatures() - 接口 中的方法org.apache.spark.ml.param.shared.HasNumFeatures
-
- getNumFeatures() - 类 中的方法org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
-
The dimension of training features.
- getNumFolds() - 接口 中的方法org.apache.spark.ml.tuning.CrossValidatorParams
-
- getNumHashTables() - 接口 中的方法org.apache.spark.ml.feature.LSHParams
-
- getNumItemBlocks() - 接口 中的方法org.apache.spark.ml.recommendation.ALSParams
-
- getNumIterations() - 类 中的方法org.apache.spark.mllib.tree.configuration.BoostingStrategy
-
- getNumObjFields() - 类 中的方法org.apache.spark.serializer.SerializationDebugger.ObjectStreamClassMethods
-
- getNumPartitions() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return the number of partitions in this RDD.
- getNumPartitions() - 接口 中的方法org.apache.spark.ml.feature.Word2VecBase
-
- getNumPartitions() - 接口 中的方法org.apache.spark.ml.fpm.FPGrowthParams
-
- getNumPartitions() - 类 中的方法org.apache.spark.rdd.RDD
-
Returns the number of partitions of this RDD.
- getNumTopFeatures() - 接口 中的方法org.apache.spark.ml.feature.ChiSqSelectorParams
-
- getNumTrees() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
Number of trees in ensemble
- getNumTrees() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
Number of trees in ensemble
- getNumTrees() - 接口 中的方法org.apache.spark.ml.tree.RandomForestParams
-
- getNumUserBlocks() - 接口 中的方法org.apache.spark.ml.recommendation.ALSParams
-
- getNumValues() - 类 中的方法org.apache.spark.ml.attribute.NominalAttribute
-
Get the number of values, either from numValues or from values.
- getObjectInspector(String, Option<Configuration>) - 类 中的静态方法org.apache.spark.sql.hive.orc.OrcFileOperator
-
- getObjFieldValues(Object, Object[]) - 类 中的方法org.apache.spark.serializer.SerializationDebugger.ObjectStreamClassMethods
-
- getOffset() - 接口 中的方法org.apache.spark.sql.connector.read.streaming.ContinuousPartitionReader
-
Get the offset of the current record, or the start offset if no records have been read.
- getOffsetCol() - 接口 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionBase
-
- getOldBoostingStrategy(Map<Object, Object>, Enumeration.Value) - 接口 中的方法org.apache.spark.ml.tree.GBTParams
-
(private[ml]) Create a BoostingStrategy instance to use with the old API.
- getOldDocConcentration() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
Get docConcentration used by spark.mllib LDA
- getOldImpurity() - 接口 中的方法org.apache.spark.ml.tree.HasVarianceImpurity
-
Convert new impurity to old impurity.
- getOldImpurity() - 接口 中的方法org.apache.spark.ml.tree.TreeClassifierParams
-
Convert new impurity to old impurity.
- getOldLossType() - 接口 中的方法org.apache.spark.ml.tree.GBTClassifierParams
-
(private[ml]) Convert new loss to old loss.
- getOldLossType() - 接口 中的方法org.apache.spark.ml.tree.GBTParams
-
Get old Gradient Boosting Loss type
- getOldLossType() - 接口 中的方法org.apache.spark.ml.tree.GBTRegressorParams
-
(private[ml]) Convert new loss to old loss.
- getOldOptimizer() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
- getOldStrategy(Map<Object, Object>, int, Enumeration.Value, Impurity, double) - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
(private[ml]) Create a Strategy instance to use with the old API.
- getOldStrategy(Map<Object, Object>, int, Enumeration.Value, Impurity) - 接口 中的方法org.apache.spark.ml.tree.TreeEnsembleParams
-
Create a Strategy instance to use with the old API.
- getOldTopicConcentration() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
Get topicConcentration used by spark.mllib LDA
- getOptimizeDocConcentration() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
- getOptimizeDocConcentration() - 类 中的方法org.apache.spark.mllib.clustering.OnlineLDAOptimizer
-
Optimize docConcentration, indicates whether docConcentration (Dirichlet parameter for
document-topic distribution) will be optimized during training.
- getOptimizer() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
- getOptimizer() - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
:: DeveloperApi ::
LDAOptimizer used to perform the actual calculation
- getOption(String) - 类 中的方法org.apache.spark.SparkConf
-
Get a parameter as an Option
- getOption(String) - 类 中的方法org.apache.spark.sql.RuntimeConfig
-
Returns the value of Spark runtime configuration property for the given key.
- getOption() - 接口 中的方法org.apache.spark.sql.streaming.GroupState
-
Get the state value as a scala Option.
- getOption() - 类 中的方法org.apache.spark.streaming.State
-
Get the state as a scala.Option.
- getOrCreate(SparkConf) - 类 中的静态方法org.apache.spark.SparkContext
-
This function may be used to get or instantiate a SparkContext and register it as a
singleton object.
- getOrCreate() - 类 中的静态方法org.apache.spark.SparkContext
-
This function may be used to get or instantiate a SparkContext and register it as a
singleton object.
- getOrCreate() - 类 中的方法org.apache.spark.sql.SparkSession.Builder
-
Gets an existing
SparkSession or, if there is no existing one, creates a new
one based on the options set in this builder.
- getOrCreate(String, Function0<JavaStreamingContext>) - 类 中的静态方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Either recreate a StreamingContext from checkpoint data or create a new StreamingContext.
- getOrCreate(String, Function0<JavaStreamingContext>, Configuration) - 类 中的静态方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Either recreate a StreamingContext from checkpoint data or create a new StreamingContext.
- getOrCreate(String, Function0<JavaStreamingContext>, Configuration, boolean) - 类 中的静态方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Either recreate a StreamingContext from checkpoint data or create a new StreamingContext.
- getOrCreate(String, Function0<StreamingContext>, Configuration, boolean) - 类 中的静态方法org.apache.spark.streaming.StreamingContext
-
Either recreate a StreamingContext from checkpoint data or create a new StreamingContext.
- getOrCreateSparkSession(JavaSparkContext, Map<Object, Object>, boolean) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
- getOrDefault(Param<T>) - 接口 中的方法org.apache.spark.ml.param.Params
-
Gets the value of a param in the embedded param map or its default value.
- getOrDiscoverAllResources(SparkConf, String, Option<String>) - 类 中的静态方法org.apache.spark.resource.ResourceUtils
-
Gets all allocated resource information for the input component from input resources file and
discover the remaining via discovery scripts.
- getOrElse(Param<T>, T) - 类 中的方法org.apache.spark.ml.param.ParamMap
-
Returns the value associated with a param or a default value.
- getOrigin(Row) - 类 中的静态方法org.apache.spark.ml.image.ImageSchema
-
Gets the origin of the image
- getOutputAttrGroupFromData(Dataset<?>, Seq<String>, Seq<String>, boolean) - 类 中的静态方法org.apache.spark.ml.feature.OneHotEncoderCommon
-
This method is called when we want to generate AttributeGroup from actual data for
one-hot encoder.
- getOutputCol() - 接口 中的方法org.apache.spark.ml.param.shared.HasOutputCol
-
- getOutputCols() - 接口 中的方法org.apache.spark.ml.param.shared.HasOutputCols
-
- getOutputSize(int) - 接口 中的方法org.apache.spark.ml.ann.Layer
-
Returns the output size given the input size (not counting the stack size).
- getOutputStream(String, Configuration) - 类 中的静态方法org.apache.spark.streaming.util.HdfsUtils
-
- getP() - 类 中的方法org.apache.spark.ml.feature.Normalizer
-
- getParallelism() - 接口 中的方法org.apache.spark.ml.param.shared.HasParallelism
-
- getParam(String) - 接口 中的方法org.apache.spark.ml.param.Params
-
Gets a param by its name.
- getParameter(String) - 类 中的方法org.apache.spark.ui.XssSafeRequest
-
- getParameterMap() - 类 中的方法org.apache.spark.ui.XssSafeRequest
-
- getParameterNames() - 类 中的方法org.apache.spark.ui.XssSafeRequest
-
- getParameterValues(String) - 类 中的方法org.apache.spark.ui.XssSafeRequest
-
- getParents(int) - 类 中的方法org.apache.spark.NarrowDependency
-
Get the parent partitions for a child partition.
- getParents(int) - 类 中的方法org.apache.spark.OneToOneDependency
-
- getParents(int) - 类 中的方法org.apache.spark.RangeDependency
-
- getPartition(long, long, int) - 类 中的方法org.apache.spark.graphx.PartitionStrategy.CanonicalRandomVertexCut$
-
- getPartition(long, long, int) - 类 中的方法org.apache.spark.graphx.PartitionStrategy.EdgePartition1D$
-
- getPartition(long, long, int) - 类 中的方法org.apache.spark.graphx.PartitionStrategy.EdgePartition2D$
-
- getPartition(long, long, int) - 接口 中的方法org.apache.spark.graphx.PartitionStrategy
-
Returns the partition number for a given edge.
- getPartition(long, long, int) - 类 中的方法org.apache.spark.graphx.PartitionStrategy.RandomVertexCut$
-
- getPartition(Object) - 类 中的方法org.apache.spark.HashPartitioner
-
- getPartition(Object) - 类 中的方法org.apache.spark.Partitioner
-
- getPartition(Object) - 类 中的方法org.apache.spark.RangePartitioner
-
- getPartition(String, String, Map<String, String>) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Returns the specified partition, or throws `NoSuchPartitionException`.
- getPartitionId() - 类 中的静态方法org.apache.spark.TaskContext
-
Returns the partition id of currently active TaskContext.
- getPartitionNames(CatalogTable, Option<Map<String, String>>) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Returns the partition names for the given table that match the supplied partition spec.
- getPartitionOption(String, String, Map<String, String>) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Returns the specified partition or None if it does not exist.
- getPartitionOption(CatalogTable, Map<String, String>) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Returns the specified partition or None if it does not exist.
- getPartitions() - 类 中的方法org.apache.spark.api.r.BaseRRDD
-
- getPartitions() - 类 中的方法org.apache.spark.rdd.CoGroupedRDD
-
- getPartitions() - 类 中的方法org.apache.spark.rdd.DefaultPartitionCoalescer
-
- getPartitions() - 类 中的方法org.apache.spark.rdd.HadoopRDD
-
- getPartitions() - 类 中的方法org.apache.spark.rdd.JdbcRDD
-
- getPartitions() - 类 中的方法org.apache.spark.rdd.NewHadoopRDD
-
- getPartitions() - 类 中的方法org.apache.spark.rdd.ShuffledRDD
-
- getPartitions() - 类 中的方法org.apache.spark.rdd.UnionRDD
-
- getPartitions(String, String, Option<Map<String, String>>) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Returns the partitions for the given table that match the supplied partition spec.
- getPartitions(CatalogTable, Option<Map<String, String>>) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Returns the partitions for the given table that match the supplied partition spec.
- getPartitions() - 类 中的方法org.apache.spark.status.LiveRDD
-
- getPartitionsByFilter(CatalogTable, Seq<Expression>) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Returns partitions filtered by predicates for the given table.
- getPartitionTableScan(Expression, LogicalPlan) - 类 中的静态方法org.apache.spark.sql.dynamicpruning.PartitionPruning
-
Search the partitioned table scan for a given partition column in a logical plan
- getPartitionWriter(int) - 接口 中的方法org.apache.spark.shuffle.api.ShuffleMapOutputWriter
-
Creates a writer that can open an output stream to persist bytes targeted for a given reduce
partition id.
- getPath() - 类 中的方法org.apache.spark.input.PortableDataStream
-
- getPattern() - 类 中的方法org.apache.spark.ml.feature.RegexTokenizer
-
- GetPeers(BlockManagerId) - 类 的构造器org.apache.spark.storage.BlockManagerMessages.GetPeers
-
- GetPeers$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.GetPeers$
-
- getPercentile() - 接口 中的方法org.apache.spark.ml.feature.ChiSqSelectorParams
-
- getPersistentRDDs() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Returns a Java map of JavaRDDs that have marked themselves as persistent via cache() call.
- getPersistentRDDs() - 类 中的方法org.apache.spark.SparkContext
-
Returns an immutable map of RDDs that have marked themselves as persistent via cache() call.
- getPmml() - 接口 中的方法org.apache.spark.mllib.pmml.export.PMMLModelExport
-
- getPoissonSamplingFunction(RDD<Tuple2<K, V>>, Map<K, Object>, boolean, long, ClassTag<K>, ClassTag<V>) - 类 中的静态方法org.apache.spark.util.random.StratifiedSamplingUtils
-
Return the per partition sampling function used for sampling with replacement.
- getPoolForName(String) - 类 中的方法org.apache.spark.SparkContext
-
:: DeveloperApi ::
Return the pool associated with the given name, if one exists
- getPosition() - 类 中的方法org.apache.spark.streaming.kinesis.KinesisInitialPositions.AtTimestamp
-
- getPosition() - 类 中的方法org.apache.spark.streaming.kinesis.KinesisInitialPositions.Latest
-
- getPosition() - 类 中的方法org.apache.spark.streaming.kinesis.KinesisInitialPositions.TrimHorizon
-
- getPowerIterationClustering(int, String, int, String, String, String) - 类 中的静态方法org.apache.spark.ml.r.PowerIterationClusteringWrapper
-
- getPredictionCol() - 接口 中的方法org.apache.spark.ml.param.shared.HasPredictionCol
-
- getPreferredLocations(Partition) - 类 中的方法org.apache.spark.rdd.HadoopRDD
-
- getPreferredLocations(Partition) - 类 中的方法org.apache.spark.rdd.NewHadoopRDD
-
- getPreferredLocations(Partition) - 类 中的方法org.apache.spark.rdd.UnionRDD
-
- getPrefixSpan(double, int, double, String) - 类 中的静态方法org.apache.spark.ml.r.PrefixSpanWrapper
-
- getPrimitiveNullWritableConstantObjectInspector() - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getProbabilityCol() - 接口 中的方法org.apache.spark.ml.param.shared.HasProbabilityCol
-
- getProcessId() - 类 中的静态方法org.apache.spark.util.Utils
-
Returns the pid of this JVM process.
- getProcessName() - 类 中的静态方法org.apache.spark.util.Utils
-
Returns the name of this JVM process.
- getPropertiesFromFile(String) - 类 中的静态方法org.apache.spark.util.Utils
-
Load properties present in the given file.
- getPythonRunnerConfMap(SQLConf) - 类 中的静态方法org.apache.spark.sql.util.ArrowUtils
-
Return Map with conf settings to be used in ArrowPythonRunner
- getQuantileCalculationStrategy() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- getQuantileProbabilities() - 接口 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionParams
-
- getQuantilesCol() - 接口 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionParams
-
- getRandomSample(Seq<T>, int, Random) - 类 中的静态方法org.apache.spark.storage.BlockReplicationUtils
-
Get a random sample of size m from the elems
- getRank() - 接口 中的方法org.apache.spark.ml.recommendation.ALSParams
-
- getRatingCol() - 接口 中的方法org.apache.spark.ml.recommendation.ALSParams
-
- getRawPredictionCol() - 接口 中的方法org.apache.spark.ml.param.shared.HasRawPredictionCol
-
- getRDDStorageInfo() - 类 中的方法org.apache.spark.SparkContext
-
:: DeveloperApi ::
Return information about what RDDs are cached, if they are in mem or on disk, how much space
they take, etc.
- getReceiver() - 类 中的方法org.apache.spark.streaming.dstream.ReceiverInputDStream
-
Gets the receiver object that will be sent to the worker nodes
to receive data.
- getRegParam() - 接口 中的方法org.apache.spark.ml.param.shared.HasRegParam
-
- getRelativeError() - 接口 中的方法org.apache.spark.ml.param.shared.HasRelativeError
-
- getRemoteUser() - 类 中的方法org.apache.spark.ui.XssSafeRequest
-
- getResource(String) - 类 中的方法org.apache.spark.util.ChildFirstURLClassLoader
-
- getResources(String) - 类 中的方法org.apache.spark.util.ChildFirstURLClassLoader
-
- getRollingIntervalSecs(SparkConf, boolean) - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- getRootDirectory() - 类 中的静态方法org.apache.spark.SparkFiles
-
Get the root directory that contains files added through SparkContext.addFile().
- getRow(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarBatch
-
Returns the row in this batch at `rowId`.
- getScalingVec() - 类 中的方法org.apache.spark.ml.feature.ElementwiseProduct
-
- getSchedulableByName(String) - 接口 中的方法org.apache.spark.scheduler.Schedulable
-
- getSchedulingMode() - 类 中的方法org.apache.spark.SparkContext
-
Return current scheduling mode
- getSchemaQuery(String) - 类 中的方法org.apache.spark.sql.jdbc.AggregatedDialect
-
- getSchemaQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.DB2Dialect
-
- getSchemaQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.DerbyDialect
-
- getSchemaQuery(String) - 类 中的方法org.apache.spark.sql.jdbc.JdbcDialect
-
The SQL query that should be used to discover the schema of a table.
- getSchemaQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.MsSqlServerDialect
-
- getSchemaQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.MySQLDialect
-
- getSchemaQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.NoopDialect
-
- getSchemaQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.OracleDialect
-
- getSchemaQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.PostgresDialect
-
- getSchemaQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.TeradataDialect
-
- getSeed() - 接口 中的方法org.apache.spark.ml.param.shared.HasSeed
-
- getSeed() - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeans
-
Gets the random seed.
- getSeed() - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixture
-
Return the random seed
- getSeed() - 类 中的方法org.apache.spark.mllib.clustering.KMeans
-
The random seed for cluster initialization.
- getSeed() - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Random seed for cluster initialization.
- getSeed() - 类 中的方法org.apache.spark.mllib.clustering.LocalLDAModel
-
Random seed for cluster initialization.
- getSelectorType() - 接口 中的方法org.apache.spark.ml.feature.ChiSqSelectorParams
-
- getSeq(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i of array type as a Scala Seq.
- getSeqOp(boolean, Map<K, Object>, org.apache.spark.util.random.StratifiedSamplingUtils.RandomDataGenerator, Option<Map<K, Object>>) - 类 中的静态方法org.apache.spark.util.random.StratifiedSamplingUtils
-
Returns the function used by aggregate to collect sampling statistics for each partition.
- getSequenceCol() - 类 中的方法org.apache.spark.ml.fpm.PrefixSpan
-
- getSessionConf(SparkSession) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
- getShort(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i as a primitive short.
- getShort(int) - 类 中的方法org.apache.spark.sql.vectorized.ArrowColumnVector
-
- getShort(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- getShort(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- getShort(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Returns the short type value for rowId.
- getShorts(int, int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Gets short type values from [rowId, rowId + count).
- getShortWritable(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getShortWritableConstantObjectInspector(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getSimpleMessage() - 异常错误 中的方法org.apache.spark.sql.AnalysisException
-
- getSimpleName(Class<?>) - 类 中的静态方法org.apache.spark.util.Utils
-
Safer than Class obj's getSimpleName which may throw Malformed class name error in scala.
- getSize() - 类 中的方法org.apache.spark.ml.feature.VectorSizeHint
-
group getParam
- getSizeAsBytes(String) - 类 中的方法org.apache.spark.SparkConf
-
Get a size parameter as bytes; throws a NoSuchElementException if it's not set.
- getSizeAsBytes(String, String) - 类 中的方法org.apache.spark.SparkConf
-
Get a size parameter as bytes, falling back to a default if not set.
- getSizeAsBytes(String, long) - 类 中的方法org.apache.spark.SparkConf
-
Get a size parameter as bytes, falling back to a default if not set.
- getSizeAsGb(String) - 类 中的方法org.apache.spark.SparkConf
-
Get a size parameter as Gibibytes; throws a NoSuchElementException if it's not set.
- getSizeAsGb(String, String) - 类 中的方法org.apache.spark.SparkConf
-
Get a size parameter as Gibibytes, falling back to a default if not set.
- getSizeAsKb(String) - 类 中的方法org.apache.spark.SparkConf
-
Get a size parameter as Kibibytes; throws a NoSuchElementException if it's not set.
- getSizeAsKb(String, String) - 类 中的方法org.apache.spark.SparkConf
-
Get a size parameter as Kibibytes, falling back to a default if not set.
- getSizeAsMb(String) - 类 中的方法org.apache.spark.SparkConf
-
Get a size parameter as Mebibytes; throws a NoSuchElementException if it's not set.
- getSizeAsMb(String, String) - 类 中的方法org.apache.spark.SparkConf
-
Get a size parameter as Mebibytes, falling back to a default if not set.
- getSizeForBlock(int) - 接口 中的方法org.apache.spark.scheduler.MapStatus
-
Estimated size for the reduce block, in bytes.
- getSizeInBytes() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Gets the current size in bytes of this `Matrix`.
- getSlotDescs() - 类 中的方法org.apache.spark.serializer.SerializationDebugger.ObjectStreamClassMethods
-
- getSmoothing() - 接口 中的方法org.apache.spark.ml.classification.NaiveBayesParams
-
- getSolver() - 接口 中的方法org.apache.spark.ml.param.shared.HasSolver
-
- getSortedTaskSetQueue() - 接口 中的方法org.apache.spark.scheduler.Schedulable
-
- getSparkClassLoader() - 类 中的静态方法org.apache.spark.util.Utils
-
Get the ClassLoader which loaded Spark.
- getSparkHome() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Get Spark's home location from either a value set through the constructor,
or the spark.home Java property, or the SPARK_HOME environment variable
(in that order of preference).
- getSparkOrYarnConfig(SparkConf, String, String) - 类 中的静态方法org.apache.spark.util.Utils
-
Return the value of a config either through the SparkConf or the Hadoop configuration.
- getSparseSizeInBytes(boolean) - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Gets the size of the minimal sparse representation of this `Matrix`.
- getSplit() - 类 中的方法org.apache.spark.ml.tree.DecisionTreeModelReadWrite.SplitData
-
- getSplits() - 类 中的方法org.apache.spark.ml.feature.Bucketizer
-
- getSplitsArray() - 类 中的方法org.apache.spark.ml.feature.Bucketizer
-
- getSrcCol() - 接口 中的方法org.apache.spark.ml.clustering.PowerIterationClusteringParams
-
- getStageInfo(int) - 类 中的方法org.apache.spark.api.java.JavaSparkStatusTracker
-
Returns stage information, or null if the stage info could not be found or was
garbage collected.
- getStageInfo(int) - 类 中的方法org.apache.spark.SparkStatusTracker
-
Returns stage information, or None if the stage info could not be found or was
garbage collected.
- getStagePath(String, int, int, String) - 类 中的方法org.apache.spark.ml.Pipeline.SharedReadWrite$
-
Get path for saving the given stage.
- getStages() - 类 中的方法org.apache.spark.ml.Pipeline
-
- getStagingDir(Path, Configuration, String) - 接口 中的方法org.apache.spark.sql.hive.execution.SaveAsHiveFile
-
- getStandardization() - 接口 中的方法org.apache.spark.ml.param.shared.HasStandardization
-
- getStartOffset() - 类 中的静态方法org.apache.spark.rdd.InputFileBlockHolder
-
Returns the starting offset of the block currently being read, or -1 if it is unknown.
- getStartTimeEpoch() - 类 中的方法org.apache.spark.status.api.v1.ApplicationAttemptInfo
-
- getState() - 接口 中的方法org.apache.spark.launcher.SparkAppHandle
-
Returns the current application state.
- getState() - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Return the associated Hive SessionState of this HiveClientImpl
- getState() - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
:: DeveloperApi ::
Return the current state of the context.
- getState() - 类 中的方法org.apache.spark.streaming.StreamingContext
-
:: DeveloperApi ::
Return the current state of the context.
- getStatement() - 类 中的方法org.apache.spark.ml.feature.SQLTransformer
-
- getStderr(Process, long) - 类 中的静态方法org.apache.spark.util.Utils
-
Return the stderr of a process after waiting for the process to terminate.
- getStepSize() - 接口 中的方法org.apache.spark.ml.param.shared.HasStepSize
-
- getStopWords() - 类 中的方法org.apache.spark.ml.feature.StopWordsRemover
-
- getStorageLevel() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Get the RDD's current storage level, or StorageLevel.NONE if none is set.
- getStorageLevel() - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
- getStorageLevel() - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- getStorageLevel() - 类 中的方法org.apache.spark.rdd.RDD
-
Get the RDD's current storage level, or StorageLevel.NONE if none is set.
- GetStorageStatus$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.GetStorageStatus$
-
- getStrategy() - 接口 中的方法org.apache.spark.ml.feature.ImputerParams
-
- getString(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i as a String object.
- getString(String) - 类 中的方法org.apache.spark.sql.types.Metadata
-
Gets a String.
- getStringArray(String) - 类 中的方法org.apache.spark.sql.types.Metadata
-
Gets a String array.
- getStringIndexerOrderType() - 接口 中的方法org.apache.spark.ml.feature.RFormulaBase
-
- getStringOrderType() - 接口 中的方法org.apache.spark.ml.feature.StringIndexerBase
-
- getStringWritable(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getStringWritableConstantObjectInspector(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getStruct(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i of struct type as a
Row object.
- getStruct(int, int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- getStruct(int, int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- getStruct(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Returns the struct type value for rowId.
- getSubsamplingRate() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
- getSubsamplingRate() - 接口 中的方法org.apache.spark.ml.tree.TreeEnsembleParams
-
- getSubsamplingRate() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- getSystemProperties() - 类 中的静态方法org.apache.spark.util.Utils
-
Returns the system properties map that is thread-safe to iterator over.
- getTable(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Get the table or view with the specified name.
- getTable(String, String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Get the table or view with the specified name in the specified database.
- getTable(CaseInsensitiveStringMap) - 接口 中的方法org.apache.spark.sql.connector.catalog.TableProvider
-
Return a
Table instance to do read/write with user-specified options.
- getTable(CaseInsensitiveStringMap, StructType) - 接口 中的方法org.apache.spark.sql.connector.catalog.TableProvider
-
Return a
Table instance to do read/write with user-specified schema and options.
- getTable(String, String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Returns the specified table, or throws `NoSuchTableException`.
- getTableExistsQuery(String) - 类 中的方法org.apache.spark.sql.jdbc.AggregatedDialect
-
- getTableExistsQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.DB2Dialect
-
- getTableExistsQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.DerbyDialect
-
- getTableExistsQuery(String) - 类 中的方法org.apache.spark.sql.jdbc.JdbcDialect
-
Get the SQL query that should be used to find if the given table exists.
- getTableExistsQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.MsSqlServerDialect
-
- getTableExistsQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.MySQLDialect
-
- getTableExistsQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.NoopDialect
-
- getTableExistsQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.OracleDialect
-
- getTableExistsQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.PostgresDialect
-
- getTableExistsQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.TeradataDialect
-
- getTableNames(SparkSession, String) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
- getTableOption(String, String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Returns the metadata for the specified table or None if it doesn't exist.
- getTables(SparkSession, String) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
- getTablesByName(String, Seq<String>) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Returns metadata of existing permanent tables/views for given names.
- getTaskInfos() - 类 中的方法org.apache.spark.BarrierTaskContext
-
:: Experimental ::
Returns
BarrierTaskInfo for all tasks in this barrier stage, ordered by partition ID.
- getTau0() - 类 中的方法org.apache.spark.mllib.clustering.OnlineLDAOptimizer
-
A (positive) learning parameter that downweights early iterations.
- getThreadDump() - 类 中的静态方法org.apache.spark.util.Utils
-
Return a thread dump of all threads' stacktraces.
- getThreadDumpForThread(long) - 类 中的静态方法org.apache.spark.util.Utils
-
- getThreshold() - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
- getThreshold() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- getThreshold() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionParams
-
Get threshold for binary classification.
- getThreshold() - 接口 中的方法org.apache.spark.ml.param.shared.HasThreshold
-
- getThreshold() - 类 中的方法org.apache.spark.mllib.classification.LogisticRegressionModel
-
Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
- getThreshold() - 类 中的方法org.apache.spark.mllib.classification.SVMModel
-
Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
- getThresholds() - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
- getThresholds() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- getThresholds() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionParams
-
Get thresholds for binary or multiclass classification.
- getThresholds() - 接口 中的方法org.apache.spark.ml.param.shared.HasThresholds
-
- getThroughOrigin() - 类 中的方法org.apache.spark.ml.evaluation.RegressionEvaluator
-
- getTimeAsMs(String) - 类 中的方法org.apache.spark.SparkConf
-
Get a time parameter as milliseconds; throws a NoSuchElementException if it's not set.
- getTimeAsMs(String, String) - 类 中的方法org.apache.spark.SparkConf
-
Get a time parameter as milliseconds, falling back to a default if not set.
- getTimeAsSeconds(String) - 类 中的方法org.apache.spark.SparkConf
-
Get a time parameter as seconds; throws a NoSuchElementException if it's not set.
- getTimeAsSeconds(String, String) - 类 中的方法org.apache.spark.SparkConf
-
Get a time parameter as seconds, falling back to a default if not set.
- getTimeMillis() - 接口 中的方法org.apache.spark.util.Clock
-
- getTimer(L) - 接口 中的方法org.apache.spark.util.ListenerBus
-
Returns a CodaHale metrics Timer for measuring the listener's event processing time.
- getTimestamp(int) - 接口 中的方法org.apache.spark.sql.Row
-
Returns the value at position i of date type as java.sql.Timestamp.
- getTimestamp() - 类 中的方法org.apache.spark.streaming.kinesis.KinesisInitialPositions.AtTimestamp
-
- getTimestampWritable(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getTimestampWritableConstantObjectInspector(Object) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- getTimeZoneOffset() - 类 中的静态方法org.apache.spark.ui.UIUtils
-
- GETTING_RESULT_TIME() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- GETTING_RESULT_TIME() - 类 中的静态方法org.apache.spark.ui.jobs.TaskDetailsClassNames
-
- GETTING_RESULT_TIME() - 类 中的静态方法org.apache.spark.ui.ToolTips
-
- gettingResult() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
- gettingResultTime() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
The time when the task started remotely getting the result.
- gettingResultTime() - 类 中的方法org.apache.spark.status.api.v1.TaskData
-
- gettingResultTime() - 类 中的方法org.apache.spark.status.api.v1.TaskMetricDistributions
-
- gettingResultTime(TaskData) - 类 中的静态方法org.apache.spark.status.AppStatusUtils
-
- gettingResultTime(long, long, long) - 类 中的静态方法org.apache.spark.status.AppStatusUtils
-
- getTokenJaasParams(KafkaTokenClusterConf) - 类 中的静态方法org.apache.spark.kafka010.KafkaTokenUtil
-
- getTol() - 接口 中的方法org.apache.spark.ml.param.shared.HasTol
-
- getToLowercase() - 类 中的方法org.apache.spark.ml.feature.RegexTokenizer
-
- getTopicConcentration() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
- getTopicConcentration() - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics'
distributions over terms.
- getTopicDistributionCol() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
- getTopologyForHost(String) - 类 中的方法org.apache.spark.storage.DefaultTopologyMapper
-
- getTopologyForHost(String) - 类 中的方法org.apache.spark.storage.FileBasedTopologyMapper
-
- getTopologyForHost(String) - 类 中的方法org.apache.spark.storage.TopologyMapper
-
Gets the topology information given the host name
- getTrainRatio() - 接口 中的方法org.apache.spark.ml.tuning.TrainValidationSplitParams
-
- getTreeStrategy() - 类 中的方法org.apache.spark.mllib.tree.configuration.BoostingStrategy
-
- getTruncateQuery(String, Option<Object>) - 类 中的方法org.apache.spark.sql.jdbc.AggregatedDialect
-
The SQL query used to truncate a table.
- getTruncateQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.DB2Dialect
-
- getTruncateQuery(String, Option<Object>) - 类 中的静态方法org.apache.spark.sql.jdbc.DB2Dialect
-
- getTruncateQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.DerbyDialect
-
- getTruncateQuery(String, Option<Object>) - 类 中的静态方法org.apache.spark.sql.jdbc.DerbyDialect
-
- getTruncateQuery(String) - 类 中的方法org.apache.spark.sql.jdbc.JdbcDialect
-
The SQL query that should be used to truncate a table.
- getTruncateQuery(String, Option<Object>) - 类 中的方法org.apache.spark.sql.jdbc.JdbcDialect
-
The SQL query that should be used to truncate a table.
- getTruncateQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.MsSqlServerDialect
-
- getTruncateQuery(String, Option<Object>) - 类 中的静态方法org.apache.spark.sql.jdbc.MsSqlServerDialect
-
- getTruncateQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.MySQLDialect
-
- getTruncateQuery(String, Option<Object>) - 类 中的静态方法org.apache.spark.sql.jdbc.MySQLDialect
-
- getTruncateQuery(String) - 类 中的静态方法org.apache.spark.sql.jdbc.NoopDialect
-
- getTruncateQuery(String, Option<Object>) - 类 中的静态方法org.apache.spark.sql.jdbc.NoopDialect
-
- getTruncateQuery(String, Option<Object>) - 类 中的静态方法org.apache.spark.sql.jdbc.OracleDialect
-
The SQL query used to truncate a table.
- getTruncateQuery(String, Option<Object>) - 类 中的静态方法org.apache.spark.sql.jdbc.PostgresDialect
-
The SQL query used to truncate a table.
- getTruncateQuery(String, Option<Object>) - 类 中的静态方法org.apache.spark.sql.jdbc.TeradataDialect
-
The SQL query used to truncate a table.
- getTruncateQuery$default$2() - 类 中的静态方法org.apache.spark.sql.jdbc.DB2Dialect
-
- getTruncateQuery$default$2() - 类 中的静态方法org.apache.spark.sql.jdbc.DerbyDialect
-
- getTruncateQuery$default$2() - 类 中的静态方法org.apache.spark.sql.jdbc.MsSqlServerDialect
-
- getTruncateQuery$default$2() - 类 中的静态方法org.apache.spark.sql.jdbc.MySQLDialect
-
- getTruncateQuery$default$2() - 类 中的静态方法org.apache.spark.sql.jdbc.NoopDialect
-
- getTruncateQuery$default$2() - 类 中的静态方法org.apache.spark.sql.jdbc.OracleDialect
-
- getTruncateQuery$default$2() - 类 中的静态方法org.apache.spark.sql.jdbc.PostgresDialect
-
- getTruncateQuery$default$2() - 类 中的静态方法org.apache.spark.sql.jdbc.TeradataDialect
-
- getUDTFor(String) - 类 中的静态方法org.apache.spark.sql.types.UDTRegistration
-
Returns the Class of UserDefinedType for the name of a given user class.
- getUidMap(Params) - 类 中的静态方法org.apache.spark.ml.util.MetaAlgorithmReadWrite
-
Examine the given estimator (which may be a compound estimator) and extract a mapping
from UIDs to corresponding Params instances.
- getUiRoot(ServletContext) - 类 中的静态方法org.apache.spark.status.api.v1.UIRootFromServletContext
-
- getUpper() - 接口 中的方法org.apache.spark.ml.feature.RobustScalerParams
-
- getUpperBound(double, long, double) - 类 中的静态方法org.apache.spark.util.random.BinomialBounds
-
Returns a threshold p such that if we conduct n Bernoulli trials with success rate = p,
it is very unlikely to have less than fraction * n successes.
- getUpperBound(double) - 类 中的静态方法org.apache.spark.util.random.PoissonBounds
-
Returns a lambda such that Pr[X < s] is very small, where X ~ Pois(lambda).
- getUpperBoundsOnCoefficients() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionParams
-
- getUpperBoundsOnIntercepts() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionParams
-
- getUsedTimeNs(long) - 类 中的静态方法org.apache.spark.util.Utils
-
Return the string to tell how long has passed in milliseconds.
- getUseNodeIdCache() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- getUserCol() - 接口 中的方法org.apache.spark.ml.recommendation.ALSModelParams
-
- getUserJars(SparkConf) - 类 中的静态方法org.apache.spark.util.Utils
-
Return the jar files pointed by the "spark.jars" property.
- getUTF8String(int) - 类 中的方法org.apache.spark.sql.vectorized.ArrowColumnVector
-
- getUTF8String(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- getUTF8String(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- getUTF8String(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Returns the string type value for rowId.
- getValidationIndicatorCol() - 接口 中的方法org.apache.spark.ml.param.shared.HasValidationIndicatorCol
-
- getValidationTol() - 接口 中的方法org.apache.spark.ml.tree.GBTParams
-
- getValidationTol() - 类 中的方法org.apache.spark.mllib.tree.configuration.BoostingStrategy
-
- getValue(int) - 类 中的方法org.apache.spark.ml.attribute.NominalAttribute
-
Gets a value given its index.
- getValue() - 类 中的方法org.apache.spark.mllib.stat.test.BinarySample
-
- getValuesMap(Seq<String>) - 接口 中的方法org.apache.spark.sql.Row
-
Returns a Map consisting of names and values for the requested fieldNames
For primitive types if value is null it returns 'zero value' specific for primitive
ie. 0 for Int - use isNullAt to ensure that value is not null
- getVarianceCol() - 接口 中的方法org.apache.spark.ml.param.shared.HasVarianceCol
-
- getVariancePower() - 接口 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionBase
-
- getVectors() - 类 中的方法org.apache.spark.ml.feature.Word2VecModel
-
- getVectors() - 类 中的方法org.apache.spark.mllib.feature.Word2VecModel
-
Returns a map of words to their vector representations.
- getVectorSize() - 接口 中的方法org.apache.spark.ml.feature.Word2VecBase
-
- getVocabSize() - 接口 中的方法org.apache.spark.ml.feature.CountVectorizerParams
-
- getWeightCol() - 接口 中的方法org.apache.spark.ml.param.shared.HasWeightCol
-
- getWidth(Row) - 类 中的静态方法org.apache.spark.ml.image.ImageSchema
-
Gets the width of the image
- getWindowSize() - 接口 中的方法org.apache.spark.ml.feature.Word2VecBase
-
- getWithCentering() - 接口 中的方法org.apache.spark.ml.feature.RobustScalerParams
-
- getWithMean() - 接口 中的方法org.apache.spark.ml.feature.StandardScalerParams
-
- getWithScaling() - 接口 中的方法org.apache.spark.ml.feature.RobustScalerParams
-
- getWithStd() - 接口 中的方法org.apache.spark.ml.feature.StandardScalerParams
-
- getWritingCommand(SessionCatalog, CatalogTable, boolean) - 接口 中的方法org.apache.spark.sql.hive.execution.CreateHiveTableAsSelectBase
-
- getWritingCommand(SessionCatalog, CatalogTable, boolean) - 类 中的方法org.apache.spark.sql.hive.execution.CreateHiveTableAsSelectCommand
-
- getWritingCommand(SessionCatalog, CatalogTable, boolean) - 类 中的方法org.apache.spark.sql.hive.execution.OptimizedCreateHiveTableAsSelectCommand
-
- Gini - org.apache.spark.mllib.tree.impurity中的类
-
Class for calculating the Gini impurity
(http://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity)
during multiclass classification.
- Gini() - 类 的构造器org.apache.spark.mllib.tree.impurity.Gini
-
- GLMClassificationModel - org.apache.spark.mllib.classification.impl中的类
-
Helper class for import/export of GLM classification models.
- GLMClassificationModel() - 类 的构造器org.apache.spark.mllib.classification.impl.GLMClassificationModel
-
- GLMClassificationModel.SaveLoadV1_0$ - org.apache.spark.mllib.classification.impl中的类
-
- GLMClassificationModel.SaveLoadV1_0$.Data - org.apache.spark.mllib.classification.impl中的类
-
Model data for import/export
- GLMClassificationModel.SaveLoadV1_0$.Data$ - org.apache.spark.mllib.classification.impl中的类
-
- GLMRegressionModel - org.apache.spark.mllib.regression.impl中的类
-
Helper methods for import/export of GLM regression models.
- GLMRegressionModel() - 类 的构造器org.apache.spark.mllib.regression.impl.GLMRegressionModel
-
- GLMRegressionModel.SaveLoadV1_0$ - org.apache.spark.mllib.regression.impl中的类
-
- GLMRegressionModel.SaveLoadV1_0$.Data - org.apache.spark.mllib.regression.impl中的类
-
Model data for model import/export
- GLMRegressionModel.SaveLoadV1_0$.Data$ - org.apache.spark.mllib.regression.impl中的类
-
- glom() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return an RDD created by coalescing all elements within each partition into an array.
- glom() - 类 中的方法org.apache.spark.rdd.RDD
-
Return an RDD created by coalescing all elements within each partition into an array.
- glom() - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD is generated by applying glom() to each RDD of
this DStream.
- glom() - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream in which each RDD is generated by applying glom() to each RDD of
this DStream.
- goButtonFormPath() - 接口 中的方法org.apache.spark.ui.PagedTable
-
Returns the submission path for the "go to page #" form.
- goodnessOfFit() - 类 中的方法org.apache.spark.mllib.stat.test.ChiSqTest.NullHypothesis$
-
- GPU() - 类 中的静态方法org.apache.spark.resource.ResourceUtils
-
- grad(DenseMatrix<Object>, DenseMatrix<Object>, DenseVector<Object>) - 接口 中的方法org.apache.spark.ml.ann.LayerModel
-
Computes the gradient.
- grad() - 类 中的方法org.apache.spark.mllib.optimization.NNLS.Workspace
-
- gradient() - 接口 中的方法org.apache.spark.ml.optim.aggregator.DifferentiableLossAggregator
-
The current weighted averaged gradient.
- gradient() - 类 中的方法org.apache.spark.ml.regression.AFTAggregator
-
- Gradient - org.apache.spark.mllib.optimization中的类
-
:: DeveloperApi ::
Class used to compute the gradient for a loss function, given a single data point.
- Gradient() - 类 的构造器org.apache.spark.mllib.optimization.Gradient
-
- gradient(double, double) - 类 中的静态方法org.apache.spark.mllib.tree.loss.AbsoluteError
-
Method to calculate the gradients for the gradient boosting calculation for least
absolute error calculation.
- gradient(double, double) - 类 中的静态方法org.apache.spark.mllib.tree.loss.LogLoss
-
Method to calculate the loss gradients for the gradient boosting calculation for binary
classification
The gradient with respect to F(x) is: - 4 y / (1 + exp(2 y F(x)))
- gradient(double, double) - 接口 中的方法org.apache.spark.mllib.tree.loss.Loss
-
Method to calculate the gradients for the gradient boosting calculation.
- gradient(double, double) - 类 中的静态方法org.apache.spark.mllib.tree.loss.SquaredError
-
Method to calculate the gradients for the gradient boosting calculation for least
squares error calculation.
- GradientBoostedTrees - org.apache.spark.ml.tree.impl中的类
-
- GradientBoostedTrees() - 类 的构造器org.apache.spark.ml.tree.impl.GradientBoostedTrees
-
- GradientBoostedTrees - org.apache.spark.mllib.tree中的类
-
- GradientBoostedTrees(BoostingStrategy) - 类 的构造器org.apache.spark.mllib.tree.GradientBoostedTrees
-
- GradientBoostedTreesModel - org.apache.spark.mllib.tree.model中的类
-
Represents a gradient boosted trees model.
- GradientBoostedTreesModel(Enumeration.Value, DecisionTreeModel[], double[]) - 类 的构造器org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
-
- GradientDescent - org.apache.spark.mllib.optimization中的类
-
Class used to solve an optimization problem using Gradient Descent.
- gradientSumArray() - 接口 中的方法org.apache.spark.ml.optim.aggregator.DifferentiableLossAggregator
-
Array of gradient values that are mutated when new instances are added to the aggregator.
- Graph<VD,ED> - org.apache.spark.graphx中的类
-
The Graph abstractly represents a graph with arbitrary objects
associated with vertices and edges.
- GraphGenerators - org.apache.spark.graphx.util中的类
-
A collection of graph generating functions.
- GraphGenerators() - 类 的构造器org.apache.spark.graphx.util.GraphGenerators
-
- GraphImpl<VD,ED> - org.apache.spark.graphx.impl中的类
-
An implementation of
Graph to support computation on graphs.
- GraphLoader - org.apache.spark.graphx中的类
-
Provides utilities for loading
Graphs from files.
- GraphLoader() - 类 的构造器org.apache.spark.graphx.GraphLoader
-
- GraphOps<VD,ED> - org.apache.spark.graphx中的类
-
Contains additional functionality for
Graph.
- GraphOps(Graph<VD, ED>, ClassTag<VD>, ClassTag<ED>) - 类 的构造器org.apache.spark.graphx.GraphOps
-
- graphToGraphOps(Graph<VD, ED>, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.Graph
-
Implicitly extracts the
GraphOps member from a graph.
- GraphXUtils - org.apache.spark.graphx中的类
-
- GraphXUtils() - 类 的构造器org.apache.spark.graphx.GraphXUtils
-
- greater(Duration) - 类 中的方法org.apache.spark.streaming.Duration
-
- greater(Time) - 类 中的方法org.apache.spark.streaming.Time
-
- greaterEq(Duration) - 类 中的方法org.apache.spark.streaming.Duration
-
- greaterEq(Time) - 类 中的方法org.apache.spark.streaming.Time
-
- GreaterThan - org.apache.spark.sql.sources中的类
-
A filter that evaluates to true iff the attribute evaluates to a value
greater than value.
- GreaterThan(String, Object) - 类 的构造器org.apache.spark.sql.sources.GreaterThan
-
- GreaterThanOrEqual - org.apache.spark.sql.sources中的类
-
A filter that evaluates to true iff the attribute evaluates to a value
greater than or equal to value.
- GreaterThanOrEqual(String, Object) - 类 的构造器org.apache.spark.sql.sources.GreaterThanOrEqual
-
- greatest(Column...) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the greatest value of the list of values, skipping null values.
- greatest(String, String...) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the greatest value of the list of column names, skipping null values.
- greatest(Seq<Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the greatest value of the list of values, skipping null values.
- greatest(String, Seq<String>) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the greatest value of the list of column names, skipping null values.
- gridGraph(SparkContext, int, int) - 类 中的静态方法org.apache.spark.graphx.util.GraphGenerators
-
Create rows by cols grid graph with each vertex connected to its
row+1 and col+1 neighbors.
- groupArr() - 类 中的方法org.apache.spark.rdd.DefaultPartitionCoalescer
-
- groupBy(Function<T, U>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return an RDD of grouped elements.
- groupBy(Function<T, U>, int) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return an RDD of grouped elements.
- groupBy(Function1<T, K>, ClassTag<K>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return an RDD of grouped items.
- groupBy(Function1<T, K>, int, ClassTag<K>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return an RDD of grouped elements.
- groupBy(Function1<T, K>, Partitioner, ClassTag<K>, Ordering<K>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return an RDD of grouped items.
- groupBy(Column...) - 类 中的方法org.apache.spark.sql.Dataset
-
Groups the Dataset using the specified columns, so we can run aggregation on them.
- groupBy(String, String...) - 类 中的方法org.apache.spark.sql.Dataset
-
Groups the Dataset using the specified columns, so that we can run aggregation on them.
- groupBy(Seq<Column>) - 类 中的方法org.apache.spark.sql.Dataset
-
Groups the Dataset using the specified columns, so we can run aggregation on them.
- groupBy(String, Seq<String>) - 类 中的方法org.apache.spark.sql.Dataset
-
Groups the Dataset using the specified columns, so that we can run aggregation on them.
- groupByKey(Partitioner) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Group the values for each key in the RDD into a single sequence.
- groupByKey(int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Group the values for each key in the RDD into a single sequence.
- groupByKey() - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Group the values for each key in the RDD into a single sequence.
- groupByKey(Partitioner) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Group the values for each key in the RDD into a single sequence.
- groupByKey(int) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Group the values for each key in the RDD into a single sequence.
- groupByKey() - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Group the values for each key in the RDD into a single sequence.
- groupByKey(Function1<T, K>, Encoder<K>) - 类 中的方法org.apache.spark.sql.Dataset
-
- groupByKey(MapFunction<T, K>, Encoder<K>) - 类 中的方法org.apache.spark.sql.Dataset
-
- groupByKey() - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying groupByKey to each RDD.
- groupByKey(int) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying groupByKey to each RDD.
- groupByKey(Partitioner) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying groupByKey on each RDD of this DStream.
- groupByKey() - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying groupByKey to each RDD.
- groupByKey(int) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying groupByKey to each RDD.
- groupByKey(Partitioner) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying groupByKey on each RDD.
- groupByKeyAndWindow(Duration) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying groupByKey over a sliding window.
- groupByKeyAndWindow(Duration, Duration) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying groupByKey over a sliding window.
- groupByKeyAndWindow(Duration, Duration, int) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying groupByKey over a sliding window on this DStream.
- groupByKeyAndWindow(Duration, Duration, Partitioner) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying groupByKey over a sliding window on this DStream.
- groupByKeyAndWindow(Duration) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying groupByKey over a sliding window.
- groupByKeyAndWindow(Duration, Duration) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying groupByKey over a sliding window.
- groupByKeyAndWindow(Duration, Duration, int) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying groupByKey over a sliding window on this DStream.
- groupByKeyAndWindow(Duration, Duration, Partitioner) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Create a new DStream by applying groupByKey over a sliding window on this DStream.
- GroupByType$() - 类 的构造器org.apache.spark.sql.RelationalGroupedDataset.GroupByType$
-
- groupEdges(Function2<ED, ED, ED>) - 类 中的方法org.apache.spark.graphx.Graph
-
Merges multiple edges between two vertices into a single edge.
- groupEdges(Function2<ED, ED, ED>) - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- groupHash() - 类 中的方法org.apache.spark.rdd.DefaultPartitionCoalescer
-
- grouping(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: indicates whether a specified column in a GROUP BY list is aggregated
or not, returns 1 for aggregated or 0 for not aggregated in the result set.
- grouping(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: indicates whether a specified column in a GROUP BY list is aggregated
or not, returns 1 for aggregated or 0 for not aggregated in the result set.
- grouping_id(Seq<Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the level of grouping, equals to
(grouping(c1) <<; (n-1)) + (grouping(c2) <<; (n-2)) + ... + grouping(cn)
- grouping_id(String, Seq<String>) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the level of grouping, equals to
(grouping(c1) <<; (n-1)) + (grouping(c2) <<; (n-2)) + ... + grouping(cn)
- GroupMappingServiceProvider - org.apache.spark.security中的接口
-
This Spark trait is used for mapping a given userName to a set of groups which it belongs to.
- GroupState<S> - org.apache.spark.sql.streaming中的接口
-
:: Experimental ::
Wrapper class for interacting with per-group state data in mapGroupsWithState and
flatMapGroupsWithState operations on KeyValueGroupedDataset.
- GroupStateTimeout - org.apache.spark.sql.streaming中的类
-
Represents the type of timeouts possible for the Dataset operations
`mapGroupsWithState` and `flatMapGroupsWithState`.
- GroupStateTimeout() - 类 的构造器org.apache.spark.sql.streaming.GroupStateTimeout
-
- groupWith(JavaPairRDD<K, W>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Alias for cogroup.
- groupWith(JavaPairRDD<K, W1>, JavaPairRDD<K, W2>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Alias for cogroup.
- groupWith(JavaPairRDD<K, W1>, JavaPairRDD<K, W2>, JavaPairRDD<K, W3>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Alias for cogroup.
- groupWith(RDD<Tuple2<K, W>>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Alias for cogroup.
- groupWith(RDD<Tuple2<K, W1>>, RDD<Tuple2<K, W2>>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Alias for cogroup.
- groupWith(RDD<Tuple2<K, W1>>, RDD<Tuple2<K, W2>>, RDD<Tuple2<K, W3>>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Alias for cogroup.
- gt(double) - 类 中的静态方法org.apache.spark.ml.param.ParamValidators
-
Check if value is greater than lowerBound
- gt(Object) - 类 中的方法org.apache.spark.sql.Column
-
Greater than.
- gt(T, T) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- gt(T, T) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- gt(double, double) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- gt(float, float) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- gt(T, T) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- gt(T, T) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- gt(T, T) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- gtEq(double) - 类 中的静态方法org.apache.spark.ml.param.ParamValidators
-
Check if value is greater than or equal to lowerBound
- gteq(T, T) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- gteq(T, T) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- gteq(double, double) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- gteq(float, float) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- gteq(T, T) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- gteq(T, T) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- gteq(T, T) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- guard(Function0<Parsers.Parser<T>>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- i() - 类 中的方法org.apache.spark.mllib.linalg.distributed.MatrixEntry
-
- id() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
A unique ID for this RDD (within its SparkContext).
- id() - 类 中的方法org.apache.spark.broadcast.Broadcast
-
- id() - 类 中的方法org.apache.spark.ml.tree.DecisionTreeModelReadWrite.NodeData
-
- id() - 类 中的方法org.apache.spark.mllib.clustering.PowerIterationClustering.Assignment
-
- id() - 类 中的方法org.apache.spark.mllib.tree.model.Node
-
- id() - 类 中的方法org.apache.spark.rdd.RDD
-
A unique ID for this RDD (within its SparkContext).
- id() - 类 中的方法org.apache.spark.scheduler.AccumulableInfo
-
- id() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
- id() - 接口 中的方法org.apache.spark.sql.streaming.StreamingQuery
-
Returns the unique id of this query that persists across restarts from checkpoint data.
- id() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryListener.QueryStartedEvent
-
- id() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryListener.QueryTerminatedEvent
-
- id() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryProgress
-
- id() - 类 中的方法org.apache.spark.status.api.v1.AccumulableInfo
-
- id() - 类 中的方法org.apache.spark.status.api.v1.ApplicationInfo
-
- id() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- id() - 类 中的方法org.apache.spark.status.api.v1.RDDStorageInfo
-
- id() - 类 中的方法org.apache.spark.storage.RDDInfo
-
- id() - 类 中的方法org.apache.spark.streaming.dstream.InputDStream
-
This is a unique identifier for the input stream.
- id() - 类 中的方法org.apache.spark.streaming.scheduler.OutputOperationInfo
-
- id() - 类 中的方法org.apache.spark.util.AccumulatorV2
-
Returns the id of this accumulator, can only be called after registration.
- Identifiable - org.apache.spark.ml.util中的接口
-
:: DeveloperApi ::
Trait for an object with an immutable unique ID that identifies itself and its derivatives.
- Identifier - org.apache.spark.sql.connector.catalog中的接口
-
Identifies an object in a catalog.
- IdentifierHelper(Identifier) - 类 的构造器org.apache.spark.sql.connector.catalog.CatalogV2Implicits.IdentifierHelper
-
- identity(String) - 类 中的静态方法org.apache.spark.sql.connector.expressions.Expressions
-
Create an identity transform for a column.
- identity(String) - 类 中的静态方法org.apache.spark.sql.connector.expressions.LogicalExpressions
-
- Identity$() - 类 的构造器org.apache.spark.ml.regression.GeneralizedLinearRegression.Identity$
-
- IDF - org.apache.spark.ml.feature中的类
-
Compute the Inverse Document Frequency (IDF) given a collection of documents.
- IDF(String) - 类 的构造器org.apache.spark.ml.feature.IDF
-
- IDF() - 类 的构造器org.apache.spark.ml.feature.IDF
-
- idf() - 类 中的方法org.apache.spark.ml.feature.IDFModel
-
Returns the IDF vector.
- IDF - org.apache.spark.mllib.feature中的类
-
Inverse document frequency (IDF).
- IDF(int) - 类 的构造器org.apache.spark.mllib.feature.IDF
-
- IDF() - 类 的构造器org.apache.spark.mllib.feature.IDF
-
- idf() - 类 中的方法org.apache.spark.mllib.feature.IDF.DocumentFrequencyAggregator
-
Returns the current IDF vector, docFreq, number of documents
- idf() - 类 中的方法org.apache.spark.mllib.feature.IDFModel
-
- IDF.DocumentFrequencyAggregator - org.apache.spark.mllib.feature中的类
-
Document frequency aggregator.
- IDFBase - org.apache.spark.ml.feature中的接口
-
- IDFModel - org.apache.spark.ml.feature中的类
-
- IDFModel - org.apache.spark.mllib.feature中的类
-
Represents an IDF model that can transform term frequency vectors.
- ifPartitionNotExists() - 类 中的方法org.apache.spark.sql.hive.execution.InsertIntoHiveTable
-
- ImageDataSource - org.apache.spark.ml.source.image中的类
-
image package implements Spark SQL data source API for loading image data as DataFrame.
- ImageDataSource() - 类 的构造器org.apache.spark.ml.source.image.ImageDataSource
-
- imageFields() - 类 中的静态方法org.apache.spark.ml.image.ImageSchema
-
- ImageSchema - org.apache.spark.ml.image中的类
-
Defines the image schema and methods to read and manipulate images.
- ImageSchema() - 类 的构造器org.apache.spark.ml.image.ImageSchema
-
- imageSchema() - 类 中的静态方法org.apache.spark.ml.image.ImageSchema
-
DataFrame with a single column of images named "image" (nullable)
- implicitPrefs() - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- implicitPrefs() - 接口 中的方法org.apache.spark.ml.recommendation.ALSParams
-
Param to decide whether to use implicit preference.
- implicits() - 类 中的方法org.apache.spark.sql.SparkSession
-
Accessor for nested Scala object
- implicits() - 类 中的方法org.apache.spark.sql.SQLContext
-
Accessor for nested Scala object
- implicits$() - 类 的构造器org.apache.spark.sql.SparkSession.implicits$
-
- implicits$() - 类 的构造器org.apache.spark.sql.SQLContext.implicits$
-
- improveException(Object, NotSerializableException) - 类 中的静态方法org.apache.spark.serializer.SerializationDebugger
-
Improve the given NotSerializableException with the serialization path leading from the given
object to the problematic object.
- Impurities - org.apache.spark.mllib.tree.impurity中的类
-
Factory for Impurity instances.
- Impurities() - 类 的构造器org.apache.spark.mllib.tree.impurity.Impurities
-
- impurity() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- impurity() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- impurity() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- impurity() - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- impurity() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- impurity() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- impurity() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- impurity() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- impurity() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- impurity() - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- impurity() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- impurity() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- impurity() - 类 中的方法org.apache.spark.ml.tree.DecisionTreeModelReadWrite.NodeData
-
- impurity() - 接口 中的方法org.apache.spark.ml.tree.HasVarianceImpurity
-
Criterion used for information gain calculation (case-insensitive).
- impurity() - 类 中的方法org.apache.spark.ml.tree.InternalNode
-
- impurity() - 类 中的方法org.apache.spark.ml.tree.LeafNode
-
- impurity() - 类 中的方法org.apache.spark.ml.tree.Node
-
Impurity measure at this node (for training data)
- impurity() - 接口 中的方法org.apache.spark.ml.tree.TreeClassifierParams
-
Criterion used for information gain calculation (case-insensitive).
- impurity() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- Impurity - org.apache.spark.mllib.tree.impurity中的接口
-
Trait for calculating information gain.
- impurity() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.NodeData
-
- impurity() - 类 中的方法org.apache.spark.mllib.tree.model.InformationGainStats
-
- impurity() - 类 中的方法org.apache.spark.mllib.tree.model.Node
-
- impurityStats() - 类 中的方法org.apache.spark.ml.tree.DecisionTreeModelReadWrite.NodeData
-
- Imputer - org.apache.spark.ml.feature中的类
-
Imputation estimator for completing missing values, either using the mean or the median
of the columns in which the missing values are located.
- Imputer(String) - 类 的构造器org.apache.spark.ml.feature.Imputer
-
- Imputer() - 类 的构造器org.apache.spark.ml.feature.Imputer
-
- ImputerModel - org.apache.spark.ml.feature中的类
-
- ImputerParams - org.apache.spark.ml.feature中的接口
-
- In() - 类 中的静态方法org.apache.spark.graphx.EdgeDirection
-
Edges arriving at a vertex.
- In - org.apache.spark.sql.sources中的类
-
A filter that evaluates to true iff the attribute evaluates to one of the values in the array.
- In(String, Object[]) - 类 的构造器org.apache.spark.sql.sources.In
-
- INACTIVE() - 类 中的静态方法org.apache.spark.streaming.scheduler.ReceiverState
-
- inArray(Object) - 类 中的静态方法org.apache.spark.ml.param.ParamValidators
-
Check for value in an allowed set of values.
- inArray(List<T>) - 类 中的静态方法org.apache.spark.ml.param.ParamValidators
-
Check for value in an allowed set of values.
- InBlock$() - 类 的构造器org.apache.spark.ml.recommendation.ALS.InBlock$
-
- InboxMessage - org.apache.spark.rpc.netty中的接口
-
- IncompatibleMergeException - org.apache.spark.util.sketch中的异常错误
-
- IncompatibleMergeException(String) - 异常错误 的构造器org.apache.spark.util.sketch.IncompatibleMergeException
-
- incrementFetchedPartitions(int) - 类 中的静态方法org.apache.spark.metrics.source.HiveCatalogMetrics
-
- incrementFileCacheHits(int) - 类 中的静态方法org.apache.spark.metrics.source.HiveCatalogMetrics
-
- incrementFilesDiscovered(int) - 类 中的静态方法org.apache.spark.metrics.source.HiveCatalogMetrics
-
- incrementHiveClientCalls(int) - 类 中的静态方法org.apache.spark.metrics.source.HiveCatalogMetrics
-
- incrementParallelListingJobCount(int) - 类 中的静态方法org.apache.spark.metrics.source.HiveCatalogMetrics
-
- inDegrees() - 类 中的方法org.apache.spark.graphx.GraphOps
-
- independence() - 类 中的方法org.apache.spark.mllib.stat.test.ChiSqTest.NullHypothesis$
-
- INDETERMINATE() - 类 中的静态方法org.apache.spark.rdd.DeterministicLevel
-
- index() - 类 中的方法org.apache.spark.ml.attribute.Attribute
-
Index of the attribute.
- INDEX() - 类 中的静态方法org.apache.spark.ml.attribute.AttributeKeys
-
- index() - 类 中的方法org.apache.spark.ml.attribute.BinaryAttribute
-
- index() - 类 中的方法org.apache.spark.ml.attribute.NominalAttribute
-
- index() - 类 中的方法org.apache.spark.ml.attribute.NumericAttribute
-
- index() - 类 中的静态方法org.apache.spark.ml.attribute.UnresolvedAttribute
-
- index(int, int) - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Return the index for the (i, j)-th element in the backing array.
- index() - 类 中的方法org.apache.spark.mllib.linalg.distributed.IndexedRow
-
- index(int, int) - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
Return the index for the (i, j)-th element in the backing array.
- index() - 接口 中的方法org.apache.spark.Partition
-
Get the partition's index within its parent RDD
- index() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
The index of this task within its task set.
- index() - 类 中的方法org.apache.spark.status.api.v1.TaskData
-
- IndexedRow - org.apache.spark.mllib.linalg.distributed中的类
-
- IndexedRow(long, Vector) - 类 的构造器org.apache.spark.mllib.linalg.distributed.IndexedRow
-
- IndexedRowMatrix - org.apache.spark.mllib.linalg.distributed中的类
-
- IndexedRowMatrix(RDD<IndexedRow>, long, int) - 类 的构造器org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
-
- IndexedRowMatrix(RDD<IndexedRow>) - 类 的构造器org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
-
Alternative constructor leaving matrix dimensions to be determined automatically.
- indexName(String) - 类 中的静态方法org.apache.spark.ui.jobs.ApiHelper
-
- indexOf(String) - 类 中的方法org.apache.spark.ml.attribute.AttributeGroup
-
Index of an attribute specified by name.
- indexOf(String) - 类 中的方法org.apache.spark.ml.attribute.NominalAttribute
-
Index of a specific value.
- indexOf(Object) - 类 中的方法org.apache.spark.ml.feature.HashingTF
-
Returns the index of the input term.
- indexOf(Object) - 类 中的方法org.apache.spark.mllib.feature.HashingTF
-
Returns the index of the input term.
- indexToLevel(int) - 类 中的静态方法org.apache.spark.mllib.tree.model.Node
-
Return the level of a tree which the given node is in.
- IndexToString - org.apache.spark.ml.feature中的类
-
A Transformer that maps a column of indices back to a new column of corresponding
string values.
- IndexToString(String) - 类 的构造器org.apache.spark.ml.feature.IndexToString
-
- IndexToString() - 类 的构造器org.apache.spark.ml.feature.IndexToString
-
- indices() - 类 中的方法org.apache.spark.ml.feature.VectorSlicer
-
An array of indices to select features from a vector column.
- indices() - 类 中的方法org.apache.spark.ml.linalg.SparseVector
-
- indices() - 类 中的方法org.apache.spark.mllib.linalg.SparseVector
-
- inferSchema(SparkSession, Map<String, String>, Seq<FileStatus>) - 类 中的方法org.apache.spark.sql.hive.execution.HiveFileFormat
-
- inferSchema(CatalogTable) - 类 中的静态方法org.apache.spark.sql.hive.HiveUtils
-
Infers the schema for Hive serde tables and returns the CatalogTable with the inferred schema.
- inferSchema(SparkSession, Map<String, String>, Seq<FileStatus>) - 类 中的方法org.apache.spark.sql.hive.orc.OrcFileFormat
-
- info() - 类 中的方法org.apache.spark.status.LiveRDD
-
- info() - 类 中的方法org.apache.spark.status.LiveStage
-
- info() - 类 中的方法org.apache.spark.status.LiveTask
-
- infoChanged(SparkAppHandle) - 接口 中的方法org.apache.spark.launcher.SparkAppHandle.Listener
-
Callback for changes in any information that is not the handle's state.
- infoGain() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.NodeData
-
- InformationGainStats - org.apache.spark.mllib.tree.model中的类
-
:: DeveloperApi ::
Information gain statistics for each split
param: gain information gain value
param: impurity current node impurity
param: leftImpurity left node impurity
param: rightImpurity right node impurity
param: leftPredict left node predict
param: rightPredict right node predict
- InformationGainStats(double, double, double, double, Predict, Predict) - 类 的构造器org.apache.spark.mllib.tree.model.InformationGainStats
-
- init(ExecutorPluginContext) - 接口 中的方法org.apache.spark.ExecutorPlugin
-
Initialize the executor plugin.
- init(FilterConfig) - 类 中的方法org.apache.spark.ui.HttpSecurityFilter
-
- initcap(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns a new string column by converting the first letter of each word to uppercase.
- initDaemon(Logger) - 类 中的静态方法org.apache.spark.util.Utils
-
Utility function that should be called early in main() for daemons to set up some common
diagnostic state.
- initHadoopOutputMetrics(TaskContext) - 类 中的静态方法org.apache.spark.internal.io.SparkHadoopWriterUtils
-
- initialHash() - 类 中的方法org.apache.spark.rdd.DefaultPartitionCoalescer
-
- initialize(boolean, SparkConf, org.apache.spark.SecurityManager) - 接口 中的方法org.apache.spark.broadcast.BroadcastFactory
-
- initialize(double, double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Binomial$
-
- initialize(double, double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Gamma$
-
- initialize(double, double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Gaussian$
-
- initialize(double, double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Poisson$
-
- initialize(RDD<Tuple2<Object, Vector>>, LDA) - 接口 中的方法org.apache.spark.mllib.clustering.LDAOptimizer
-
Initializer for the optimizer.
- initialize() - 类 中的静态方法org.apache.spark.rdd.InputFileBlockHolder
-
Initializes thread local by explicitly getting the value.
- initialize(TaskScheduler, SchedulerBackend) - 接口 中的方法org.apache.spark.scheduler.ExternalClusterManager
-
Initialize task scheduler and backend scheduler.
- initialize(String, CaseInsensitiveStringMap) - 接口 中的方法org.apache.spark.sql.connector.catalog.CatalogPlugin
-
Called to initialize configuration.
- initialize(String, CaseInsensitiveStringMap) - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- initialize(MutableAggregationBuffer) - 类 中的方法org.apache.spark.sql.expressions.UserDefinedAggregateFunction
-
Initializes the given aggregation buffer, i.e. the zero value of the aggregation buffer.
- initializeApplication() - 接口 中的方法org.apache.spark.shuffle.api.ShuffleDriverComponents
-
Called once in the driver to bootstrap this module that is specific to this application.
- Initialized() - 类 中的静态方法org.apache.spark.rdd.CheckpointState
-
- initializeExecutor(String, String, Map<String, String>) - 接口 中的方法org.apache.spark.shuffle.api.ShuffleExecutorComponents
-
Called once per executor to bootstrap this module with state that is specific to
that executor, specifically the application ID and executor ID.
- initializeLogging(boolean, boolean) - 接口 中的方法org.apache.spark.internal.Logging
-
- initializeLogIfNecessary(boolean) - 接口 中的方法org.apache.spark.internal.Logging
-
- initializeLogIfNecessary(boolean, boolean) - 接口 中的方法org.apache.spark.internal.Logging
-
- initialOffset() - 接口 中的方法org.apache.spark.sql.connector.read.streaming.SparkDataStream
-
Returns the initial offset for a streaming query to start reading from.
- initialState(RDD<Tuple2<KeyType, StateType>>) - 类 中的方法org.apache.spark.streaming.StateSpec
-
Set the RDD containing the initial states that will be used by mapWithState
- initialState(JavaPairRDD<KeyType, StateType>) - 类 中的方法org.apache.spark.streaming.StateSpec
-
Set the RDD containing the initial states that will be used by mapWithState
- initialValue() - 类 中的方法org.apache.spark.partial.PartialResult
-
- initialWeights() - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
- initialWeights() - 接口 中的方法org.apache.spark.ml.classification.MultilayerPerceptronParams
-
The initial weights of the model.
- initInputSerDe(Seq<Expression>) - 类 中的方法org.apache.spark.sql.hive.execution.HiveScriptIOSchema
-
- initMode() - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- initMode() - 类 中的方法org.apache.spark.ml.clustering.KMeansModel
-
- initMode() - 接口 中的方法org.apache.spark.ml.clustering.KMeansParams
-
Param for the initialization algorithm.
- initMode() - 类 中的方法org.apache.spark.ml.clustering.PowerIterationClustering
-
- initMode() - 接口 中的方法org.apache.spark.ml.clustering.PowerIterationClusteringParams
-
Param for the initialization algorithm.
- initModel(DenseVector<Object>, Random) - 接口 中的方法org.apache.spark.ml.ann.Layer
-
Returns the instance of the layer with random generated weights.
- initOutputFormat(JobContext) - 类 中的方法org.apache.spark.internal.io.HadoopWriteConfigUtil
-
- initOutputSerDe(Seq<Attribute>) - 类 中的方法org.apache.spark.sql.hive.execution.HiveScriptIOSchema
-
- initSteps() - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- initSteps() - 类 中的方法org.apache.spark.ml.clustering.KMeansModel
-
- initSteps() - 接口 中的方法org.apache.spark.ml.clustering.KMeansParams
-
Param for the number of steps for the k-means|| initialization mode.
- initWriter(TaskAttemptContext, int) - 类 中的方法org.apache.spark.internal.io.HadoopWriteConfigUtil
-
- injectCheckRule(Function1<SparkSession, Function1<LogicalPlan, BoxedUnit>>) - 类 中的方法org.apache.spark.sql.SparkSessionExtensions
-
Inject an check analysis
Rule builder into the
SparkSession.
- injectColumnar(Function1<SparkSession, ColumnarRule>) - 类 中的方法org.apache.spark.sql.SparkSessionExtensions
-
Inject a rule that can override the columnar execution of an executor.
- injectFunction(Tuple3<FunctionIdentifier, ExpressionInfo, Function1<Seq<Expression>, Expression>>) - 类 中的方法org.apache.spark.sql.SparkSessionExtensions
-
Injects a custom function into the FunctionRegistry
at runtime for all sessions.
- injectOptimizerRule(Function1<SparkSession, Rule<LogicalPlan>>) - 类 中的方法org.apache.spark.sql.SparkSessionExtensions
-
- injectParser(Function2<SparkSession, ParserInterface, ParserInterface>) - 类 中的方法org.apache.spark.sql.SparkSessionExtensions
-
- injectPlannerStrategy(Function1<SparkSession, SparkStrategy>) - 类 中的方法org.apache.spark.sql.SparkSessionExtensions
-
- injectPostHocResolutionRule(Function1<SparkSession, Rule<LogicalPlan>>) - 类 中的方法org.apache.spark.sql.SparkSessionExtensions
-
- injectResolutionRule(Function1<SparkSession, Rule<LogicalPlan>>) - 类 中的方法org.apache.spark.sql.SparkSessionExtensions
-
Inject an analyzer resolution
Rule builder into the
SparkSession.
- InnerClosureFinder - org.apache.spark.util中的类
-
- InnerClosureFinder(Set<Class<?>>) - 类 的构造器org.apache.spark.util.InnerClosureFinder
-
- innerJoin(EdgeRDD<ED2>, Function4<Object, Object, ED, ED2, ED3>, ClassTag<ED2>, ClassTag<ED3>) - 类 中的方法org.apache.spark.graphx.EdgeRDD
-
Inner joins this EdgeRDD with another EdgeRDD, assuming both are partitioned using the same
PartitionStrategy.
- innerJoin(EdgeRDD<ED2>, Function4<Object, Object, ED, ED2, ED3>, ClassTag<ED2>, ClassTag<ED3>) - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
- innerJoin(RDD<Tuple2<Object, U>>, Function3<Object, VD, U, VD2>, ClassTag<U>, ClassTag<VD2>) - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- innerJoin(RDD<Tuple2<Object, U>>, Function3<Object, VD, U, VD2>, ClassTag<U>, ClassTag<VD2>) - 类 中的方法org.apache.spark.graphx.VertexRDD
-
Inner joins this VertexRDD with an RDD containing vertex attribute pairs.
- innerZipJoin(VertexRDD<U>, Function3<Object, VD, U, VD2>, ClassTag<U>, ClassTag<VD2>) - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- innerZipJoin(VertexRDD<U>, Function3<Object, VD, U, VD2>, ClassTag<U>, ClassTag<VD2>) - 类 中的方法org.apache.spark.graphx.VertexRDD
-
Efficiently inner joins this VertexRDD with another VertexRDD sharing the same index.
- inPlace() - 接口 中的方法org.apache.spark.ml.ann.Layer
-
If true, the memory is not allocated for the output of this layer.
- InProcessLauncher - org.apache.spark.launcher中的类
-
In-process launcher for Spark applications.
- InProcessLauncher() - 类 的构造器org.apache.spark.launcher.InProcessLauncher
-
- input() - 类 中的方法org.apache.spark.ml.TransformStart
-
- input() - 类 中的方法org.apache.spark.sql.hive.execution.ScriptTransformationExec
-
- INPUT() - 类 中的静态方法org.apache.spark.ui.ToolTips
-
- input$() - 类 的构造器org.apache.spark.InternalAccumulator.input$
-
- input_file_name() - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a string column for the file name of the current Spark task.
- INPUT_FORMAT() - 类 中的静态方法org.apache.spark.sql.hive.execution.HiveOptions
-
- INPUT_METRICS_PREFIX() - 类 中的静态方法org.apache.spark.InternalAccumulator
-
- INPUT_RECORDS() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- INPUT_SIZE() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- inputBytes() - 类 中的方法org.apache.spark.status.api.v1.ExecutorStageSummary
-
- inputBytes() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.Binarizer
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.Bucketizer
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.CountVectorizer
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.CountVectorizerModel
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.HashingTF
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.IDF
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.IDFModel
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.Imputer
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.ImputerModel
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.IndexToString
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.MaxAbsScaler
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.MaxAbsScalerModel
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.MinMaxScaler
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.MinMaxScalerModel
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.OneHotEncoder
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.OneHotEncoderModel
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.PCA
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.PCAModel
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.QuantileDiscretizer
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.RobustScaler
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.RobustScalerModel
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.StandardScaler
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.StandardScalerModel
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.StopWordsRemover
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.StringIndexer
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.StringIndexerModel
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.VectorIndexer
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.VectorIndexerModel
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.VectorSizeHint
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.VectorSlicer
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- inputCol() - 类 中的方法org.apache.spark.ml.feature.Word2VecModel
-
- inputCol() - 接口 中的方法org.apache.spark.ml.param.shared.HasInputCol
-
Param for input column name.
- inputCol() - 类 中的方法org.apache.spark.ml.UnaryTransformer
-
- inputCols() - 类 中的方法org.apache.spark.ml.feature.Binarizer
-
- inputCols() - 类 中的方法org.apache.spark.ml.feature.Bucketizer
-
- inputCols() - 类 中的方法org.apache.spark.ml.feature.FeatureHasher
-
- inputCols() - 类 中的方法org.apache.spark.ml.feature.Imputer
-
- inputCols() - 类 中的方法org.apache.spark.ml.feature.ImputerModel
-
- inputCols() - 类 中的方法org.apache.spark.ml.feature.Interaction
-
- inputCols() - 类 中的方法org.apache.spark.ml.feature.OneHotEncoder
-
- inputCols() - 类 中的方法org.apache.spark.ml.feature.OneHotEncoderModel
-
- inputCols() - 类 中的方法org.apache.spark.ml.feature.QuantileDiscretizer
-
- inputCols() - 类 中的方法org.apache.spark.ml.feature.StringIndexer
-
- inputCols() - 类 中的方法org.apache.spark.ml.feature.StringIndexerModel
-
- inputCols() - 类 中的方法org.apache.spark.ml.feature.VectorAssembler
-
- inputCols() - 接口 中的方法org.apache.spark.ml.param.shared.HasInputCols
-
Param for input column names.
- inputDStream() - 类 中的方法org.apache.spark.streaming.api.java.JavaInputDStream
-
- inputDStream() - 类 中的方法org.apache.spark.streaming.api.java.JavaPairInputDStream
-
- InputDStream<T> - org.apache.spark.streaming.dstream中的类
-
This is the abstract base class for all input streams.
- InputDStream(StreamingContext, ClassTag<T>) - 类 的构造器org.apache.spark.streaming.dstream.InputDStream
-
- InputFileBlockHolder - org.apache.spark.rdd中的类
-
This holds file names of the current Spark task.
- InputFileBlockHolder() - 类 的构造器org.apache.spark.rdd.InputFileBlockHolder
-
- inputFiles() - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a best-effort snapshot of the files that compose this Dataset.
- inputFormat() - 类 中的方法org.apache.spark.sql.hive.execution.HiveOptions
-
- inputFormatClazz() - 类 中的方法org.apache.spark.scheduler.InputFormatInfo
-
- inputFormatClazz() - 类 中的方法org.apache.spark.scheduler.SplitInfo
-
- InputFormatInfo - org.apache.spark.scheduler中的类
-
:: DeveloperApi ::
Parses and holds information about inputFormat (and files) specified as a parameter.
- InputFormatInfo(Configuration, Class<?>, String) - 类 的构造器org.apache.spark.scheduler.InputFormatInfo
-
- InputMetricDistributions - org.apache.spark.status.api.v1中的类
-
- InputMetrics - org.apache.spark.status.api.v1中的类
-
- inputMetrics() - 类 中的方法org.apache.spark.status.api.v1.TaskMetricDistributions
-
- inputMetrics() - 类 中的方法org.apache.spark.status.api.v1.TaskMetrics
-
- InputPartition - org.apache.spark.sql.connector.read中的接口
-
- inputRecords() - 类 中的方法org.apache.spark.status.api.v1.ExecutorStageSummary
-
- inputRecords() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- inputRowFormat() - 类 中的方法org.apache.spark.sql.hive.execution.HiveScriptIOSchema
-
- inputRowFormatMap() - 类 中的方法org.apache.spark.sql.hive.execution.HiveScriptIOSchema
-
- inputRowsPerSecond() - 类 中的方法org.apache.spark.sql.streaming.SourceProgress
-
- inputRowsPerSecond() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryProgress
-
The aggregate (across all sources) rate of data arriving.
- inputSchema() - 类 中的方法org.apache.spark.sql.expressions.UserDefinedAggregateFunction
-
A StructType represents data types of input arguments of this aggregate function.
- inputSerdeClass() - 类 中的方法org.apache.spark.sql.hive.execution.HiveScriptIOSchema
-
- inputSerdeProps() - 类 中的方法org.apache.spark.sql.hive.execution.HiveScriptIOSchema
-
- inputSize() - 类 中的方法org.apache.spark.status.api.v1.streaming.BatchInfo
-
- inputStreamId() - 类 中的方法org.apache.spark.streaming.scheduler.StreamInputInfo
-
- inRange(double, double, boolean, boolean) - 类 中的静态方法org.apache.spark.ml.param.ParamValidators
-
Check for value in range lowerBound to upperBound.
- inRange(double, double) - 类 中的静态方法org.apache.spark.ml.param.ParamValidators
-
Version of `inRange()` which uses inclusive be default: [lowerBound, upperBound]
- insert(Dataset<Row>, boolean) - 接口 中的方法org.apache.spark.sql.sources.InsertableRelation
-
- InsertableRelation - org.apache.spark.sql.sources中的接口
-
A BaseRelation that can be used to insert data into it through the insert method.
- insertInto(String) - 类 中的方法org.apache.spark.sql.DataFrameWriter
-
Inserts the content of the DataFrame to the specified table.
- InsertIntoHiveDirCommand - org.apache.spark.sql.hive.execution中的类
-
Command for writing the results of query to file system.
- InsertIntoHiveDirCommand(boolean, CatalogStorageFormat, LogicalPlan, boolean, Seq<String>) - 类 的构造器org.apache.spark.sql.hive.execution.InsertIntoHiveDirCommand
-
- InsertIntoHiveTable - org.apache.spark.sql.hive.execution中的类
-
Command for writing data out to a Hive table.
- InsertIntoHiveTable(CatalogTable, Map<String, Option<String>>, LogicalPlan, boolean, boolean, Seq<String>) - 类 的构造器org.apache.spark.sql.hive.execution.InsertIntoHiveTable
-
- inShutdown() - 类 中的静态方法org.apache.spark.util.ShutdownHookManager
-
Detect whether this thread might be executing a shutdown hook.
- inspectorToDataType(ObjectInspector) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- inspectorToDataType(ObjectInspector) - 类 中的静态方法org.apache.spark.sql.hive.orc.OrcFileFormat
-
- instance() - 类 中的方法org.apache.spark.ml.LoadInstanceEnd
-
- instance() - 类 中的静态方法org.apache.spark.mllib.tree.impurity.Entropy
-
Get this impurity instance.
- instance() - 类 中的静态方法org.apache.spark.mllib.tree.impurity.Gini
-
Get this impurity instance.
- instance() - 类 中的静态方法org.apache.spark.mllib.tree.impurity.Variance
-
Get this impurity instance.
- INSTANCE - 类 中的静态变量org.apache.spark.serializer.DummySerializerInstance
-
- INSTANT() - 类 中的静态方法org.apache.spark.sql.Encoders
-
Creates an encoder that serializes instances of the java.time.Instant class
to the internal representation of nullable Catalyst's TimestampType.
- instantiate(String, String, String, boolean) - 类 中的静态方法org.apache.spark.internal.io.FileCommitProtocol
-
Instantiates a FileCommitProtocol using the given className.
- instr(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Locate the position of the first occurrence of substr column in the given string.
- INT() - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for nullable int type.
- IntArrayParam - org.apache.spark.ml.param中的类
-
:: DeveloperApi ::
Specialized version of Param[Array[Int} for Java.
- IntArrayParam(Params, String, String, Function1<int[], Object>) - 类 的构造器org.apache.spark.ml.param.IntArrayParam
-
- IntArrayParam(Params, String, String) - 类 的构造器org.apache.spark.ml.param.IntArrayParam
-
- IntegerExactNumeric - org.apache.spark.sql.types中的类
-
- IntegerExactNumeric() - 类 的构造器org.apache.spark.sql.types.IntegerExactNumeric
-
- IntegerType - 类 中的静态变量org.apache.spark.sql.types.DataTypes
-
Gets the IntegerType object.
- IntegerType - org.apache.spark.sql.types中的类
-
The data type representing Int values.
- IntegerType() - 类 的构造器org.apache.spark.sql.types.IntegerType
-
- INTER_JOB_WAIT_MS() - 类 中的静态方法org.apache.spark.ui.UIWorkloadGenerator
-
- interact(Term) - 类 中的静态方法org.apache.spark.ml.feature.Dot
-
- interact(Term) - 类 中的静态方法org.apache.spark.ml.feature.EmptyTerm
-
- interact(Term) - 接口 中的方法org.apache.spark.ml.feature.InteractableTerm
-
Interactions of interactable terms.
- interact(Term) - 接口 中的方法org.apache.spark.ml.feature.Term
-
Default interactions of a Term
- InteractableTerm - org.apache.spark.ml.feature中的接口
-
A term that may be part of an interaction, e.g.
- Interaction - org.apache.spark.ml.feature中的类
-
Implements the feature interaction transform.
- Interaction(String) - 类 的构造器org.apache.spark.ml.feature.Interaction
-
- Interaction() - 类 的构造器org.apache.spark.ml.feature.Interaction
-
- intercept() - 类 中的方法org.apache.spark.ml.classification.LinearSVCModel
-
- intercept() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
The model intercept for "binomial" logistic regression.
- intercept() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- intercept() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
- intercept() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionModel
-
- intercept() - 类 中的方法org.apache.spark.mllib.classification.impl.GLMClassificationModel.SaveLoadV1_0$.Data
-
- intercept() - 类 中的方法org.apache.spark.mllib.classification.LogisticRegressionModel
-
- intercept() - 类 中的方法org.apache.spark.mllib.classification.SVMModel
-
- intercept() - 类 中的方法org.apache.spark.mllib.regression.GeneralizedLinearModel
-
- intercept() - 类 中的方法org.apache.spark.mllib.regression.impl.GLMRegressionModel.SaveLoadV1_0$.Data
-
- intercept() - 类 中的方法org.apache.spark.mllib.regression.LassoModel
-
- intercept() - 类 中的方法org.apache.spark.mllib.regression.LinearRegressionModel
-
- intercept() - 类 中的方法org.apache.spark.mllib.regression.RidgeRegressionModel
-
- interceptVector() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- intermediateStorageLevel() - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- intermediateStorageLevel() - 接口 中的方法org.apache.spark.ml.recommendation.ALSParams
-
Param for StorageLevel for intermediate datasets.
- InternalAccumulator - org.apache.spark中的类
-
A collection of fields and methods concerned with internal accumulators that represent
task level metrics.
- InternalAccumulator() - 类 的构造器org.apache.spark.InternalAccumulator
-
- InternalAccumulator.input$ - org.apache.spark中的类
-
- InternalAccumulator.output$ - org.apache.spark中的类
-
- InternalAccumulator.shuffleRead$ - org.apache.spark中的类
-
- InternalAccumulator.shuffleWrite$ - org.apache.spark中的类
-
- InternalKMeansModelWriter - org.apache.spark.ml.clustering中的类
-
A writer for KMeans that handles the "internal" (or default) format
- InternalKMeansModelWriter() - 类 的构造器org.apache.spark.ml.clustering.InternalKMeansModelWriter
-
- InternalLinearRegressionModelWriter - org.apache.spark.ml.regression中的类
-
A writer for LinearRegression that handles the "internal" (or default) format
- InternalLinearRegressionModelWriter() - 类 的构造器org.apache.spark.ml.regression.InternalLinearRegressionModelWriter
-
- InternalNode - org.apache.spark.ml.tree中的类
-
Internal Decision Tree node.
- InterruptibleIterator<T> - org.apache.spark中的类
-
:: DeveloperApi ::
An iterator that wraps around an existing iterator to provide task killing functionality.
- InterruptibleIterator(TaskContext, Iterator<T>) - 类 的构造器org.apache.spark.InterruptibleIterator
-
- interruptThread() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.KillTask
-
- interruptThread() - 类 中的方法org.apache.spark.scheduler.local.KillTask
-
- intersect(Dataset<T>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset containing rows only in both this Dataset and another Dataset.
- intersectAll(Dataset<T>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset containing rows only in both this Dataset and another Dataset while
preserving the duplicates.
- intersection(JavaDoubleRDD) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Return the intersection of this RDD and another one.
- intersection(JavaPairRDD<K, V>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return the intersection of this RDD and another one.
- intersection(JavaRDD<T>) - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Return the intersection of this RDD and another one.
- intersection(RDD<T>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return the intersection of this RDD and another one.
- intersection(RDD<T>, Partitioner, Ordering<T>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return the intersection of this RDD and another one.
- intersection(RDD<T>, int) - 类 中的方法org.apache.spark.rdd.RDD
-
Return the intersection of this RDD and another one.
- IntParam - org.apache.spark.ml.param中的类
-
:: DeveloperApi ::
Specialized version of Param[Int] for Java.
- IntParam(String, String, String, Function1<Object, Object>) - 类 的构造器org.apache.spark.ml.param.IntParam
-
- IntParam(String, String, String) - 类 的构造器org.apache.spark.ml.param.IntParam
-
- IntParam(Identifiable, String, String, Function1<Object, Object>) - 类 的构造器org.apache.spark.ml.param.IntParam
-
- IntParam(Identifiable, String, String) - 类 的构造器org.apache.spark.ml.param.IntParam
-
- IntParam - org.apache.spark.util中的类
-
An extractor object for parsing strings into integers.
- IntParam() - 类 的构造器org.apache.spark.util.IntParam
-
- invalidateSerializedMapOutputStatusCache() - 类 中的方法org.apache.spark.ShuffleStatus
-
Clears the cached serialized map output statuses.
- invalidateTable(Identifier) - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- invalidateTable(Identifier) - 接口 中的方法org.apache.spark.sql.connector.catalog.TableCatalog
-
Invalidate cached table metadata for an
identifier.
- inverse() - 类 中的方法org.apache.spark.ml.feature.DCT
-
Indicates whether to perform the inverse DCT (true) or forward DCT (false).
- inverse(double[], int) - 类 中的静态方法org.apache.spark.mllib.linalg.CholeskyDecomposition
-
Computes the inverse of a real symmetric positive definite matrix A
using the Cholesky factorization A = U**T*U.
- Inverse$() - 类 的构造器org.apache.spark.ml.regression.GeneralizedLinearRegression.Inverse$
-
- invokedMethod(Object, Class<?>, String) - 类 中的静态方法org.apache.spark.graphx.util.BytecodeUtils
-
Test whether the given closure invokes the specified method in the specified class.
- invokeWriteReplace(Object) - 类 中的方法org.apache.spark.serializer.SerializationDebugger.ObjectStreamClassMethods
-
- ioEncryptionKey() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.SparkAppConfig
-
- ioschema() - 类 中的方法org.apache.spark.sql.hive.execution.ScriptTransformationExec
-
- is32BitDecimalType(DataType) - 类 中的静态方法org.apache.spark.sql.types.DecimalType
-
Returns if dt is a DecimalType that fits inside an int
- is64BitDecimalType(DataType) - 类 中的静态方法org.apache.spark.sql.types.DecimalType
-
Returns if dt is a DecimalType that fits inside a long
- IS_TESTING() - 类 中的静态方法org.apache.spark.internal.config.Tests
-
- isActive() - 接口 中的方法org.apache.spark.sql.streaming.StreamingQuery
-
Returns true if this query is actively running.
- isActive() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- isActive() - 类 中的方法org.apache.spark.status.api.v1.streaming.ReceiverInfo
-
- isActive() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- isAddIntercept() - 类 中的方法org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
-
Get if the algorithm uses addIntercept
- isAllowed(Enumeration.Value, Enumeration.Value) - 类 中的静态方法org.apache.spark.scheduler.TaskLocality
-
- isBarrier() - 类 中的方法org.apache.spark.storage.RDDInfo
-
- isBatchingEnabled(SparkConf, boolean) - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- isBindCollision(Throwable) - 类 中的静态方法org.apache.spark.util.Utils
-
Return whether the exception is caused by an address-port collision when binding.
- isBlacklisted() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- isBlacklisted() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- isBlacklisted() - 类 中的方法org.apache.spark.status.LiveExecutorStageSummary
-
- isBlacklistedForStage() - 类 中的方法org.apache.spark.status.api.v1.ExecutorStageSummary
-
- isBroadcast() - 类 中的方法org.apache.spark.storage.BlockId
-
- isBucket() - 类 中的方法org.apache.spark.sql.catalog.Column
-
- isByteArrayDecimalType(DataType) - 类 中的静态方法org.apache.spark.sql.types.DecimalType
-
Returns if dt is a DecimalType that doesn't fit inside a long
- isCached(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Returns true if the table is currently cached in-memory.
- isCached(String) - 类 中的方法org.apache.spark.sql.SQLContext
-
Returns true if the table is currently cached in-memory.
- isCached() - 类 中的方法org.apache.spark.storage.BlockStatus
-
- isCached() - 类 中的方法org.apache.spark.storage.RDDInfo
-
- isCancelled() - 类 中的方法org.apache.spark.ComplexFutureAction
-
- isCancelled() - 接口 中的方法org.apache.spark.FutureAction
-
Returns whether the action has been cancelled.
- isCancelled() - 类 中的方法org.apache.spark.SimpleFutureAction
-
- isCascadingTruncateTable() - 类 中的方法org.apache.spark.sql.jdbc.AggregatedDialect
-
- isCascadingTruncateTable() - 类 中的静态方法org.apache.spark.sql.jdbc.DB2Dialect
-
- isCascadingTruncateTable() - 类 中的静态方法org.apache.spark.sql.jdbc.DerbyDialect
-
- isCascadingTruncateTable() - 类 中的方法org.apache.spark.sql.jdbc.JdbcDialect
-
Return Some[true] iff TRUNCATE TABLE causes cascading default.
- isCascadingTruncateTable() - 类 中的静态方法org.apache.spark.sql.jdbc.MsSqlServerDialect
-
- isCascadingTruncateTable() - 类 中的静态方法org.apache.spark.sql.jdbc.MySQLDialect
-
- isCascadingTruncateTable() - 类 中的静态方法org.apache.spark.sql.jdbc.NoopDialect
-
- isCascadingTruncateTable() - 类 中的静态方法org.apache.spark.sql.jdbc.OracleDialect
-
- isCascadingTruncateTable() - 类 中的静态方法org.apache.spark.sql.jdbc.PostgresDialect
-
- isCascadingTruncateTable() - 类 中的静态方法org.apache.spark.sql.jdbc.TeradataDialect
-
- isCheckpointed() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return whether this RDD has been checkpointed or not
- isCheckpointed() - 类 中的方法org.apache.spark.graphx.Graph
-
Return whether this Graph has been checkpointed or not.
- isCheckpointed() - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
- isCheckpointed() - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- isCheckpointed() - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- isCheckpointed() - 类 中的方法org.apache.spark.rdd.RDD
-
Return whether this RDD is checkpointed and materialized, either reliably or locally.
- isClientMode(SparkConf) - 类 中的静态方法org.apache.spark.util.Utils
-
- isCliSessionState() - 类 中的静态方法org.apache.spark.sql.hive.HiveUtils
-
Check current Thread's SessionState type
- isColMajor() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Indicates whether the values backing this matrix are arranged in column major order.
- isCompatible(BloomFilter) - 类 中的方法org.apache.spark.util.sketch.BloomFilter
-
Determines whether a given bloom filter is compatible with this bloom filter.
- isCompleted() - 类 中的方法org.apache.spark.BarrierTaskContext
-
- isCompleted() - 类 中的方法org.apache.spark.ComplexFutureAction
-
- isCompleted() - 接口 中的方法org.apache.spark.FutureAction
-
Returns whether the action has already been completed with a value or an exception.
- isCompleted() - 类 中的方法org.apache.spark.SimpleFutureAction
-
- isCompleted() - 类 中的方法org.apache.spark.TaskContext
-
Returns true if the task has completed.
- isConnectorUsingCurrentToken(Map<String, Object>, Option<KafkaTokenClusterConf>) - 类 中的静态方法org.apache.spark.kafka010.KafkaTokenUtil
-
- isDataAvailable() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryStatus
-
- isDefined(Param<?>) - 接口 中的方法org.apache.spark.ml.param.Params
-
Checks whether a param is explicitly set or has a default value.
- isDistributed() - 类 中的方法org.apache.spark.ml.clustering.DistributedLDAModel
-
- isDistributed() - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
- isDistributed() - 类 中的方法org.apache.spark.ml.clustering.LocalLDAModel
-
- isDriver() - 类 中的方法org.apache.spark.storage.BlockManagerId
-
- isDynamicAllocationEnabled(SparkConf) - 类 中的静态方法org.apache.spark.util.Utils
-
Return whether dynamic allocation is enabled in the given conf.
- isEmpty() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
- isEmpty() - 类 中的方法org.apache.spark.rdd.RDD
-
- isEmpty() - 类 中的方法org.apache.spark.sql.Dataset
-
Returns true if the Dataset is empty.
- isEmpty() - 类 中的方法org.apache.spark.sql.util.CaseInsensitiveStringMap
-
- isEncryptionEnabled(JavaSparkContext) - 类 中的静态方法org.apache.spark.api.r.RUtils
-
- isExecutorActive(String) - 接口 中的方法org.apache.spark.ExecutorAllocationClient
-
Whether an executor is active.
- IsExecutorAlive(String) - 类 的构造器org.apache.spark.storage.BlockManagerMessages.IsExecutorAlive
-
- IsExecutorAlive$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.IsExecutorAlive$
-
- isExecutorStartupConf(String) - 类 中的静态方法org.apache.spark.SparkConf
-
Return whether the given config should be passed to an executor on start-up.
- isExperiment() - 类 中的方法org.apache.spark.mllib.stat.test.BinarySample
-
- isFailed(Enumeration.Value) - 类 中的静态方法org.apache.spark.TaskState
-
- isFatalError(Throwable) - 类 中的静态方法org.apache.spark.util.Utils
-
Returns true if the given exception was fatal.
- isFile(Path) - 类 中的静态方法org.apache.spark.ml.image.SamplePathFilter
-
- isFileSplittable(Path, CompressionCodecFactory) - 类 中的静态方法org.apache.spark.util.Utils
-
Check whether the file of the path is splittable.
- isFinal() - 枚举 中的方法org.apache.spark.launcher.SparkAppHandle.State
-
Whether this state is a final state, meaning the application is not running anymore
once it's reached.
- isFinished(Enumeration.Value) - 类 中的静态方法org.apache.spark.TaskState
-
- isGlobalJaasConfigurationProvided() - 类 中的静态方法org.apache.spark.kafka010.KafkaTokenUtil
-
- isHive23() - 类 中的静态方法org.apache.spark.sql.hive.HiveUtils
-
- isIgnorableException(Throwable) - 接口 中的方法org.apache.spark.util.ListenerBus
-
Allows bus implementations to prevent error logging for certain exceptions.
- isin(Object...) - 类 中的方法org.apache.spark.sql.Column
-
A boolean expression that is evaluated to true if the value of this expression is contained
by the evaluated values of the arguments.
- isin(Seq<Object>) - 类 中的方法org.apache.spark.sql.Column
-
A boolean expression that is evaluated to true if the value of this expression is contained
by the evaluated values of the arguments.
- isInCollection(Iterable<?>) - 类 中的方法org.apache.spark.sql.Column
-
A boolean expression that is evaluated to true if the value of this expression is contained
by the provided collection.
- isInCollection(Iterable<?>) - 类 中的方法org.apache.spark.sql.Column
-
A boolean expression that is evaluated to true if the value of this expression is contained
by the provided collection.
- isInDirectory(File, File) - 类 中的静态方法org.apache.spark.util.Utils
-
Return whether the specified file is a parent directory of the child file.
- isInitialValueFinal() - 类 中的方法org.apache.spark.partial.PartialResult
-
- isInterrupted() - 类 中的方法org.apache.spark.BarrierTaskContext
-
- isInterrupted() - 类 中的方法org.apache.spark.TaskContext
-
Returns true if the task has been killed.
- isLargerBetter() - 类 中的方法org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
-
- isLargerBetter() - 类 中的方法org.apache.spark.ml.evaluation.ClusteringEvaluator
-
- isLargerBetter() - 类 中的方法org.apache.spark.ml.evaluation.Evaluator
-
Indicates whether the metric returned by evaluate should be maximized (true, default)
or minimized (false).
- isLargerBetter() - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- isLargerBetter() - 类 中的方法org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator
-
- isLargerBetter() - 类 中的方法org.apache.spark.ml.evaluation.RankingEvaluator
-
- isLargerBetter() - 类 中的方法org.apache.spark.ml.evaluation.RegressionEvaluator
-
- isLeaf() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.NodeData
-
- isLeaf() - 类 中的方法org.apache.spark.mllib.tree.model.Node
-
- isLeftChild(int) - 类 中的静态方法org.apache.spark.mllib.tree.model.Node
-
Returns true if this is a left child.
- isLocal() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
- isLocal - 类 中的变量org.apache.spark.ExecutorPluginContext
-
- isLocal() - 类 中的方法org.apache.spark.SparkContext
-
- isLocal() - 类 中的方法org.apache.spark.sql.Dataset
-
Returns true if the collect and take methods can be run locally
(without any Spark executors).
- isLocal() - 类 中的方法org.apache.spark.sql.hive.execution.InsertIntoHiveDirCommand
-
- isLocalMaster(SparkConf) - 类 中的静态方法org.apache.spark.util.Utils
-
- isLocalUri(String) - 类 中的静态方法org.apache.spark.util.Utils
-
Returns whether the URI is a "local:" URI.
- isMac() - 类 中的静态方法org.apache.spark.util.Utils
-
Whether the underlying operating system is Mac OS X.
- isModifiable(String) - 类 中的方法org.apache.spark.sql.RuntimeConfig
-
Indicates whether the configuration property with the given key
is modifiable in the current session.
- isMulticlassClassification() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- isMulticlassWithCategoricalFeatures() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- isMultipleOf(Duration) - 类 中的方法org.apache.spark.streaming.Duration
-
- isMultipleOf(Duration) - 类 中的方法org.apache.spark.streaming.Time
-
- isNaN() - 类 中的方法org.apache.spark.sql.Column
-
True if the current expression is NaN.
- isnan(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Return true iff the column is NaN.
- isNominal() - 类 中的方法org.apache.spark.ml.attribute.Attribute
-
- isNominal() - 类 中的方法org.apache.spark.ml.attribute.BinaryAttribute
-
- isNominal() - 类 中的方法org.apache.spark.ml.attribute.NominalAttribute
-
- isNominal() - 类 中的方法org.apache.spark.ml.attribute.NumericAttribute
-
- isNominal() - 类 中的静态方法org.apache.spark.ml.attribute.UnresolvedAttribute
-
- isNotNull() - 类 中的方法org.apache.spark.sql.Column
-
True if the current expression is NOT null.
- IsNotNull - org.apache.spark.sql.sources中的类
-
A filter that evaluates to true iff the attribute evaluates to a non-null value.
- IsNotNull(String) - 类 的构造器org.apache.spark.sql.sources.IsNotNull
-
- isNull() - 类 中的方法org.apache.spark.sql.Column
-
True if the current expression is null.
- isnull(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Return true iff the column is null.
- IsNull - org.apache.spark.sql.sources中的类
-
A filter that evaluates to true iff the attribute evaluates to null.
- IsNull(String) - 类 的构造器org.apache.spark.sql.sources.IsNull
-
- isNullable() - 类 中的方法org.apache.spark.sql.connector.catalog.TableChange.AddColumn
-
- isNullable() - 类 中的方法org.apache.spark.sql.connector.catalog.TableChange.UpdateColumnType
-
- isNullAt(int) - 接口 中的方法org.apache.spark.sql.Row
-
Checks whether the value at position i is null.
- isNullAt(int) - 类 中的方法org.apache.spark.sql.vectorized.ArrowColumnVector
-
- isNullAt(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- isNullAt(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- isNullAt(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnVector
-
Returns whether the value at rowId is NULL.
- isNumeric() - 类 中的方法org.apache.spark.ml.attribute.Attribute
-
- isNumeric() - 类 中的方法org.apache.spark.ml.attribute.BinaryAttribute
-
- isNumeric() - 类 中的方法org.apache.spark.ml.attribute.NominalAttribute
-
- isNumeric() - 类 中的方法org.apache.spark.ml.attribute.NumericAttribute
-
- isNumeric() - 类 中的静态方法org.apache.spark.ml.attribute.UnresolvedAttribute
-
- IsolatedRpcEndpoint - org.apache.spark.rpc中的接口
-
An endpoint that uses a dedicated thread pool for delivering messages.
- isOpen() - 类 中的方法org.apache.spark.security.CryptoStreamUtils.ErrorHandlingReadableChannel
-
- isOpen() - 类 中的方法org.apache.spark.storage.CountingWritableChannel
-
- isOrdinal() - 类 中的方法org.apache.spark.ml.attribute.NominalAttribute
-
- isotonic() - 类 中的方法org.apache.spark.ml.regression.IsotonicRegression
-
- isotonic() - 接口 中的方法org.apache.spark.ml.regression.IsotonicRegressionBase
-
Param for whether the output sequence should be isotonic/increasing (true) or
antitonic/decreasing (false).
- isotonic() - 类 中的方法org.apache.spark.ml.regression.IsotonicRegressionModel
-
- isotonic() - 类 中的方法org.apache.spark.mllib.regression.IsotonicRegressionModel
-
- IsotonicRegression - org.apache.spark.ml.regression中的类
-
Isotonic regression.
- IsotonicRegression(String) - 类 的构造器org.apache.spark.ml.regression.IsotonicRegression
-
- IsotonicRegression() - 类 的构造器org.apache.spark.ml.regression.IsotonicRegression
-
- IsotonicRegression - org.apache.spark.mllib.regression中的类
-
Isotonic regression.
- IsotonicRegression() - 类 的构造器org.apache.spark.mllib.regression.IsotonicRegression
-
Constructs IsotonicRegression instance with default parameter isotonic = true.
- IsotonicRegressionBase - org.apache.spark.ml.regression中的接口
-
Params for isotonic regression.
- IsotonicRegressionModel - org.apache.spark.ml.regression中的类
-
Model fitted by IsotonicRegression.
- IsotonicRegressionModel - org.apache.spark.mllib.regression中的类
-
Regression model for isotonic regression.
- IsotonicRegressionModel(double[], double[], boolean) - 类 的构造器org.apache.spark.mllib.regression.IsotonicRegressionModel
-
- IsotonicRegressionModel(Iterable<Object>, Iterable<Object>, Boolean) - 类 的构造器org.apache.spark.mllib.regression.IsotonicRegressionModel
-
A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter.
- isOutputSpecValidationEnabled(SparkConf) - 类 中的静态方法org.apache.spark.internal.io.SparkHadoopWriterUtils
-
- isPartition() - 类 中的方法org.apache.spark.sql.catalog.Column
-
- isPresent() - 类 中的方法org.apache.spark.api.java.Optional
-
- isProcessRunning(int) - 类 中的静态方法org.apache.spark.util.Utils
-
Given a process id, return true if the process is still running.
- isRDD() - 类 中的方法org.apache.spark.storage.BlockId
-
- isReady() - 接口 中的方法org.apache.spark.scheduler.SchedulerBackend
-
- isRegistered() - 类 中的方法org.apache.spark.util.AccumulatorV2
-
Returns true if this accumulator has been registered.
- isRInstalled() - 类 中的静态方法org.apache.spark.api.r.RUtils
-
Check if R is installed before running tests that use R commands.
- isRowMajor() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Indicates whether the values backing this matrix are arranged in row major order.
- isSessionCatalog(CatalogPlugin) - 类 中的静态方法org.apache.spark.sql.connector.catalog.CatalogV2Util
-
- isSet(Param<?>) - 接口 中的方法org.apache.spark.ml.param.Params
-
Checks whether a param is explicitly set.
- isShuffle() - 类 中的方法org.apache.spark.storage.BlockId
-
- isSparkPortConf(String) - 类 中的静态方法org.apache.spark.SparkConf
-
Return true if the given config matches either spark.*.port or spark.port.
- isSparkRInstalled() - 类 中的静态方法org.apache.spark.api.r.RUtils
-
Check if SparkR is installed before running tests that use SparkR.
- isSplitable(SparkSession, Map<String, String>, Path) - 类 中的方法org.apache.spark.sql.hive.orc.OrcFileFormat
-
- isStarted() - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Check if the receiver has started or not.
- isStopped() - 类 中的方法org.apache.spark.SparkContext
-
- isStopped() - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Check if receiver has been marked for stopping.
- isStreaming() - 类 中的方法org.apache.spark.sql.Dataset
-
Returns true if this Dataset contains one or more sources that continuously
return data as it arrives.
- isStreamingDynamicAllocationEnabled(SparkConf) - 类 中的静态方法org.apache.spark.util.Utils
-
- isSubClassOf(Type, Class<?>) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- isSubDir(Path, Path, FileSystem) - 接口 中的方法org.apache.spark.sql.hive.execution.SaveAsHiveFile
-
- isTemporary() - 类 中的方法org.apache.spark.sql.catalog.Function
-
- isTemporary() - 类 中的方法org.apache.spark.sql.catalog.Table
-
- isTesting() - 类 中的静态方法org.apache.spark.util.Utils
-
Indicates whether Spark is currently running unit tests.
- isTimingOut() - 类 中的方法org.apache.spark.streaming.State
-
Whether the state is timing out and going to be removed by the system after the current batch.
- isTraceEnabled() - 接口 中的方法org.apache.spark.internal.Logging
-
- isTransposed() - 类 中的方法org.apache.spark.ml.linalg.DenseMatrix
-
- isTransposed() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Flag that keeps track whether the matrix is transposed or not.
- isTransposed() - 类 中的方法org.apache.spark.ml.linalg.SparseMatrix
-
- isTransposed() - 类 中的方法org.apache.spark.mllib.linalg.DenseMatrix
-
- isTransposed() - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
Flag that keeps track whether the matrix is transposed or not.
- isTransposed() - 类 中的方法org.apache.spark.mllib.linalg.SparseMatrix
-
- isTriggerActive() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryStatus
-
- isValid() - 类 中的方法org.apache.spark.ml.param.Param
-
- isValid() - 类 中的方法org.apache.spark.storage.StorageLevel
-
- isWindows() - 类 中的静态方法org.apache.spark.util.Utils
-
Whether the underlying operating system is Windows.
- isZero() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- isZero() - 类 中的方法org.apache.spark.streaming.Duration
-
- isZero() - 类 中的方法org.apache.spark.util.AccumulatorV2
-
Returns if this accumulator is zero value or not. e.g. for a counter accumulator, 0 is zero
value; for a list accumulator, Nil is zero value.
- isZero() - 类 中的方法org.apache.spark.util.CollectionAccumulator
-
Returns false if this accumulator instance has any values in it.
- isZero() - 类 中的方法org.apache.spark.util.DoubleAccumulator
-
Returns false if this accumulator has had any values added to it or the sum is non-zero.
- isZero() - 类 中的方法org.apache.spark.util.LongAccumulator
-
Returns false if this accumulator has had any values added to it or the sum is non-zero.
- item() - 类 中的方法org.apache.spark.ml.recommendation.ALS.Rating
-
- itemCol() - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- itemCol() - 类 中的方法org.apache.spark.ml.recommendation.ALSModel
-
- itemCol() - 接口 中的方法org.apache.spark.ml.recommendation.ALSModelParams
-
Param for the column name for item ids.
- itemFactors() - 类 中的方法org.apache.spark.ml.recommendation.ALSModel
-
- items() - 类 中的方法org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
-
- itemsCol() - 类 中的方法org.apache.spark.ml.fpm.FPGrowth
-
- itemsCol() - 类 中的方法org.apache.spark.ml.fpm.FPGrowthModel
-
- itemsCol() - 接口 中的方法org.apache.spark.ml.fpm.FPGrowthParams
-
Items column name.
- itemSupport() - 类 中的方法org.apache.spark.mllib.fpm.FPGrowthModel
-
- iterator(Partition, TaskContext) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Internal method to this RDD; will read from cache if applicable, or otherwise compute it.
- iterator(Partition, TaskContext) - 类 中的方法org.apache.spark.rdd.RDD
-
Internal method to this RDD; will read from cache if applicable, or otherwise compute it.
- iterator() - 类 中的方法org.apache.spark.sql.types.StructType
-
- iterator() - 类 中的方法org.apache.spark.status.RDDPartitionSeq
-
- IV_LENGTH_IN_BYTES() - 类 中的静态方法org.apache.spark.security.CryptoStreamUtils
-
- L1Updater - org.apache.spark.mllib.optimization中的类
-
:: DeveloperApi ::
Updater for L1 regularized problems.
- L1Updater() - 类 的构造器org.apache.spark.mllib.optimization.L1Updater
-
- label() - 类 中的方法org.apache.spark.ml.feature.LabeledPoint
-
- label() - 类 中的方法org.apache.spark.mllib.regression.LabeledPoint
-
- labelCol() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionSummary
-
Field in "predictions" which gives the true label of each instance (if available).
- labelCol() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionSummaryImpl
-
- labelCol() - 类 中的方法org.apache.spark.ml.classification.OneVsRest
-
- labelCol() - 类 中的方法org.apache.spark.ml.classification.OneVsRestModel
-
- labelCol() - 类 中的方法org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
-
- labelCol() - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- labelCol() - 类 中的方法org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator
-
- labelCol() - 类 中的方法org.apache.spark.ml.evaluation.RankingEvaluator
-
- labelCol() - 类 中的方法org.apache.spark.ml.evaluation.RegressionEvaluator
-
- labelCol() - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- labelCol() - 类 中的方法org.apache.spark.ml.feature.ChiSqSelectorModel
-
- labelCol() - 类 中的方法org.apache.spark.ml.feature.RFormula
-
- labelCol() - 类 中的方法org.apache.spark.ml.feature.RFormulaModel
-
- labelCol() - 接口 中的方法org.apache.spark.ml.param.shared.HasLabelCol
-
Param for label column name.
- labelCol() - 类 中的方法org.apache.spark.ml.PredictionModel
-
- labelCol() - 类 中的方法org.apache.spark.ml.Predictor
-
- labelCol() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- labelCol() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- labelCol() - 类 中的方法org.apache.spark.ml.regression.IsotonicRegression
-
- labelCol() - 类 中的方法org.apache.spark.ml.regression.IsotonicRegressionModel
-
- labelCol() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionSummary
-
- LabeledPoint - org.apache.spark.ml.feature中的类
-
Class that represents the features and label of a data point.
- LabeledPoint(double, Vector) - 类 的构造器org.apache.spark.ml.feature.LabeledPoint
-
- LabeledPoint - org.apache.spark.mllib.regression中的类
-
Class that represents the features and labels of a data point.
- LabeledPoint(double, Vector) - 类 的构造器org.apache.spark.mllib.regression.LabeledPoint
-
- LabelPropagation - org.apache.spark.graphx.lib中的类
-
Label Propagation algorithm.
- LabelPropagation() - 类 的构造器org.apache.spark.graphx.lib.LabelPropagation
-
- labels() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionSummary
-
Returns the sequence of labels in ascending order.
- labels() - 类 中的方法org.apache.spark.ml.feature.IndexToString
-
Optional param for array of labels specifying index-string mapping.
- labels() - 类 中的方法org.apache.spark.ml.feature.StringIndexerModel
-
- labels() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel
-
- labels() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV1_0$.Data
-
- labels() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV2_0$.Data
-
- labels() - 类 中的方法org.apache.spark.mllib.evaluation.MulticlassMetrics
-
- labels() - 类 中的方法org.apache.spark.mllib.evaluation.MultilabelMetrics
-
- labelsArray() - 类 中的方法org.apache.spark.ml.feature.StringIndexerModel
-
- lag(Column, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Window function: returns the value that is offset rows before the current row, and
null if there is less than offset rows before the current row.
- lag(String, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Window function: returns the value that is offset rows before the current row, and
null if there is less than offset rows before the current row.
- lag(String, int, Object) - 类 中的静态方法org.apache.spark.sql.functions
-
Window function: returns the value that is offset rows before the current row, and
defaultValue if there is less than offset rows before the current row.
- lag(Column, int, Object) - 类 中的静态方法org.apache.spark.sql.functions
-
Window function: returns the value that is offset rows before the current row, and
defaultValue if there is less than offset rows before the current row.
- LassoModel - org.apache.spark.mllib.regression中的类
-
Regression model trained using Lasso.
- LassoModel(Vector, double) - 类 的构造器org.apache.spark.mllib.regression.LassoModel
-
- LassoWithSGD - org.apache.spark.mllib.regression中的类
-
Train a regression model with L1-regularization using Stochastic Gradient Descent.
- last(Column, boolean) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the last value in a group.
- last(String, boolean) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the last value of the column in a group.
- last(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the last value in a group.
- last(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the last value of the column in a group.
- last_day(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the last day of the month which the given date belongs to.
- lastDir() - 类 中的方法org.apache.spark.mllib.optimization.NNLS.Workspace
-
- lastError() - 类 中的方法org.apache.spark.status.api.v1.streaming.ReceiverInfo
-
- lastError() - 类 中的方法org.apache.spark.streaming.scheduler.ReceiverInfo
-
- lastErrorMessage() - 类 中的方法org.apache.spark.status.api.v1.streaming.ReceiverInfo
-
- lastErrorMessage() - 类 中的方法org.apache.spark.streaming.scheduler.ReceiverInfo
-
- lastErrorTime() - 类 中的方法org.apache.spark.status.api.v1.streaming.ReceiverInfo
-
- lastErrorTime() - 类 中的方法org.apache.spark.streaming.scheduler.ReceiverInfo
-
- lastProgress() - 接口 中的方法org.apache.spark.sql.streaming.StreamingQuery
-
- lastStageNameAndDescription(org.apache.spark.status.AppStatusStore, JobData) - 类 中的静态方法org.apache.spark.ui.jobs.ApiHelper
-
- lastUpdate() - 类 中的方法org.apache.spark.status.LiveRDDDistribution
-
- lastUpdated() - 类 中的方法org.apache.spark.status.api.v1.ApplicationAttemptInfo
-
- Latest() - 类 的构造器org.apache.spark.streaming.kinesis.KinesisInitialPositions.Latest
-
- latestModel() - 类 中的方法org.apache.spark.mllib.clustering.StreamingKMeans
-
Return the latest model.
- latestModel() - 类 中的方法org.apache.spark.mllib.regression.StreamingLinearAlgorithm
-
Return the latest model.
- latestOffset() - 接口 中的方法org.apache.spark.sql.connector.read.streaming.MicroBatchStream
-
Returns the most recent offset available.
- launch() - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
Launches a sub-process that will start the configured Spark application.
- LAUNCH_TIME() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- LAUNCHING() - 类 中的静态方法org.apache.spark.TaskState
-
- LaunchTask(org.apache.spark.util.SerializableBuffer) - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.LaunchTask
-
- LaunchTask$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.LaunchTask$
-
- launchTime() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
- launchTime() - 类 中的方法org.apache.spark.status.api.v1.TaskData
-
- Layer - org.apache.spark.ml.ann中的接口
-
Trait that holds Layer properties, that are needed to instantiate it.
- LayerModel - org.apache.spark.ml.ann中的接口
-
Trait that holds Layer weights (or parameters).
- layerModels() - 接口 中的方法org.apache.spark.ml.ann.TopologyModel
-
Array of layer models
- layers() - 接口 中的方法org.apache.spark.ml.ann.TopologyModel
-
Array of layers
- layers() - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel
-
- layers() - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
- layers() - 接口 中的方法org.apache.spark.ml.classification.MultilayerPerceptronParams
-
Layer sizes including input size and output size.
- LBFGS - org.apache.spark.mllib.optimization中的类
-
:: DeveloperApi ::
Class used to solve an optimization problem using Limited-memory BFGS.
- LBFGS(Gradient, Updater) - 类 的构造器org.apache.spark.mllib.optimization.LBFGS
-
- LDA - org.apache.spark.ml.clustering中的类
-
Latent Dirichlet Allocation (LDA), a topic model designed for text documents.
- LDA(String) - 类 的构造器org.apache.spark.ml.clustering.LDA
-
- LDA() - 类 的构造器org.apache.spark.ml.clustering.LDA
-
- LDA - org.apache.spark.mllib.clustering中的类
-
Latent Dirichlet Allocation (LDA), a topic model designed for text documents.
- LDA() - 类 的构造器org.apache.spark.mllib.clustering.LDA
-
Constructs a LDA instance with default parameters.
- LDAModel - org.apache.spark.ml.clustering中的类
-
- LDAModel - org.apache.spark.mllib.clustering中的类
-
Latent Dirichlet Allocation (LDA) model.
- LDAOptimizer - org.apache.spark.mllib.clustering中的接口
-
:: DeveloperApi ::
An LDAOptimizer specifies which optimization/learning/inference algorithm to use, and it can
hold optimizer-specific parameters for users to set.
- LDAParams - org.apache.spark.ml.clustering中的接口
-
- LDAUtils - org.apache.spark.mllib.clustering中的类
-
Utility methods for LDA.
- LDAUtils() - 类 的构造器org.apache.spark.mllib.clustering.LDAUtils
-
- lead(String, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Window function: returns the value that is offset rows after the current row, and
null if there is less than offset rows after the current row.
- lead(Column, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Window function: returns the value that is offset rows after the current row, and
null if there is less than offset rows after the current row.
- lead(String, int, Object) - 类 中的静态方法org.apache.spark.sql.functions
-
Window function: returns the value that is offset rows after the current row, and
defaultValue if there is less than offset rows after the current row.
- lead(Column, int, Object) - 类 中的静态方法org.apache.spark.sql.functions
-
Window function: returns the value that is offset rows after the current row, and
defaultValue if there is less than offset rows after the current row.
- leafCol() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- leafCol() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- leafCol() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- leafCol() - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- leafCol() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- leafCol() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- leafCol() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- leafCol() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- leafCol() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- leafCol() - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- leafCol() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- leafCol() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- leafCol() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
Leaf indices column name.
- leafIterator(Node) - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeModel
-
- LeafNode - org.apache.spark.ml.tree中的类
-
Decision tree leaf node.
- learningDecay() - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- learningDecay() - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
- learningDecay() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
For Online optimizer only: optimizer = "online".
- learningOffset() - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- learningOffset() - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
- learningOffset() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
For Online optimizer only: optimizer = "online".
- learningRate() - 类 中的方法org.apache.spark.mllib.tree.configuration.BoostingStrategy
-
- least(Column...) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the least value of the list of values, skipping null values.
- least(String, String...) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the least value of the list of column names, skipping null values.
- least(Seq<Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the least value of the list of values, skipping null values.
- least(String, Seq<String>) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the least value of the list of column names, skipping null values.
- LeastSquaresGradient - org.apache.spark.mllib.optimization中的类
-
:: DeveloperApi ::
Compute gradient and loss for a Least-squared loss function, as used in linear regression.
- LeastSquaresGradient() - 类 的构造器org.apache.spark.mllib.optimization.LeastSquaresGradient
-
- left() - 类 中的方法org.apache.spark.sql.sources.And
-
- left() - 类 中的方法org.apache.spark.sql.sources.Or
-
- leftCategories() - 类 中的方法org.apache.spark.ml.tree.CategoricalSplit
-
Get sorted categories which split to the left
- leftCategoriesOrThreshold() - 类 中的方法org.apache.spark.ml.tree.DecisionTreeModelReadWrite.SplitData
-
- leftChild() - 类 中的方法org.apache.spark.ml.tree.DecisionTreeModelReadWrite.NodeData
-
- leftChild() - 类 中的方法org.apache.spark.ml.tree.InternalNode
-
- leftChildIndex(int) - 类 中的静态方法org.apache.spark.mllib.tree.model.Node
-
Return the index of the left child of this node.
- leftImpurity() - 类 中的方法org.apache.spark.mllib.tree.model.InformationGainStats
-
- leftJoin(RDD<Tuple2<Object, VD2>>, Function3<Object, VD, Option<VD2>, VD3>, ClassTag<VD2>, ClassTag<VD3>) - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- leftJoin(RDD<Tuple2<Object, VD2>>, Function3<Object, VD, Option<VD2>, VD3>, ClassTag<VD2>, ClassTag<VD3>) - 类 中的方法org.apache.spark.graphx.VertexRDD
-
Left joins this VertexRDD with an RDD containing vertex attribute pairs.
- leftNode() - 类 中的方法org.apache.spark.mllib.tree.model.Node
-
- leftNodeId() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.NodeData
-
- leftOuterJoin(JavaPairRDD<K, W>, Partitioner) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Perform a left outer join of this and other.
- leftOuterJoin(JavaPairRDD<K, W>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Perform a left outer join of this and other.
- leftOuterJoin(JavaPairRDD<K, W>, int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Perform a left outer join of this and other.
- leftOuterJoin(RDD<Tuple2<K, W>>, Partitioner) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Perform a left outer join of this and other.
- leftOuterJoin(RDD<Tuple2<K, W>>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Perform a left outer join of this and other.
- leftOuterJoin(RDD<Tuple2<K, W>>, int) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Perform a left outer join of this and other.
- leftOuterJoin(JavaPairDStream<K, W>) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying 'left outer join' between RDDs of this DStream and
other DStream.
- leftOuterJoin(JavaPairDStream<K, W>, int) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying 'left outer join' between RDDs of this DStream and
other DStream.
- leftOuterJoin(JavaPairDStream<K, W>, Partitioner) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying 'left outer join' between RDDs of this DStream and
other DStream.
- leftOuterJoin(DStream<Tuple2<K, W>>, ClassTag<W>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying 'left outer join' between RDDs of this DStream and
other DStream.
- leftOuterJoin(DStream<Tuple2<K, W>>, int, ClassTag<W>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying 'left outer join' between RDDs of this DStream and
other DStream.
- leftOuterJoin(DStream<Tuple2<K, W>>, Partitioner, ClassTag<W>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying 'left outer join' between RDDs of this DStream and
other DStream.
- leftPredict() - 类 中的方法org.apache.spark.mllib.tree.model.InformationGainStats
-
- leftZipJoin(VertexRDD<VD2>, Function3<Object, VD, Option<VD2>, VD3>, ClassTag<VD2>, ClassTag<VD3>) - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- leftZipJoin(VertexRDD<VD2>, Function3<Object, VD, Option<VD2>, VD3>, ClassTag<VD2>, ClassTag<VD3>) - 类 中的方法org.apache.spark.graphx.VertexRDD
-
Left joins this RDD with another VertexRDD with the same index.
- length() - 类 中的方法org.apache.spark.scheduler.SplitInfo
-
- length(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the character length of a given string or number of bytes of a binary string.
- length() - 接口 中的方法org.apache.spark.sql.Row
-
Number of elements in the Row.
- length() - 类 中的方法org.apache.spark.sql.types.CharType
-
- length() - 类 中的方法org.apache.spark.sql.types.HiveStringType
-
- length() - 类 中的方法org.apache.spark.sql.types.StructType
-
- length() - 类 中的方法org.apache.spark.sql.types.VarcharType
-
- length() - 类 中的方法org.apache.spark.status.RDDPartitionSeq
-
- leq(Object) - 类 中的方法org.apache.spark.sql.Column
-
Less than or equal to.
- less(Duration) - 类 中的方法org.apache.spark.streaming.Duration
-
- less(Time) - 类 中的方法org.apache.spark.streaming.Time
-
- lessEq(Duration) - 类 中的方法org.apache.spark.streaming.Duration
-
- lessEq(Time) - 类 中的方法org.apache.spark.streaming.Time
-
- LessThan - org.apache.spark.sql.sources中的类
-
A filter that evaluates to true iff the attribute evaluates to a value
less than value.
- LessThan(String, Object) - 类 的构造器org.apache.spark.sql.sources.LessThan
-
- LessThanOrEqual - org.apache.spark.sql.sources中的类
-
A filter that evaluates to true iff the attribute evaluates to a value
less than or equal to value.
- LessThanOrEqual(String, Object) - 类 的构造器org.apache.spark.sql.sources.LessThanOrEqual
-
- levenshtein(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the Levenshtein distance of the two given string columns.
- libraryPathEnvName() - 类 中的静态方法org.apache.spark.util.Utils
-
Return the current system LD_LIBRARY_PATH name
- libraryPathEnvPrefix(Seq<String>) - 类 中的静态方法org.apache.spark.util.Utils
-
Return the prefix of a command that appends the given library paths to the
system-specific library path environment variable.
- LibSVMDataSource - org.apache.spark.ml.source.libsvm中的类
-
libsvm package implements Spark SQL data source API for loading LIBSVM data as DataFrame.
- LibSVMDataSource() - 类 的构造器org.apache.spark.ml.source.libsvm.LibSVMDataSource
-
- lift() - 类 中的方法org.apache.spark.mllib.fpm.AssociationRules.Rule
-
Returns the lift of the rule.
- like(String) - 类 中的方法org.apache.spark.sql.Column
-
SQL like expression.
- limit(int) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset by taking the first n rows.
- line() - 异常错误 中的方法org.apache.spark.sql.AnalysisException
-
- LinearDataGenerator - org.apache.spark.mllib.util中的类
-
:: DeveloperApi ::
Generate sample data used for Linear Data.
- LinearDataGenerator() - 类 的构造器org.apache.spark.mllib.util.LinearDataGenerator
-
- LinearRegression - org.apache.spark.ml.regression中的类
-
Linear regression.
- LinearRegression(String) - 类 的构造器org.apache.spark.ml.regression.LinearRegression
-
- LinearRegression() - 类 的构造器org.apache.spark.ml.regression.LinearRegression
-
- LinearRegressionModel - org.apache.spark.ml.regression中的类
-
- LinearRegressionModel - org.apache.spark.mllib.regression中的类
-
Regression model trained using LinearRegression.
- LinearRegressionModel(Vector, double) - 类 的构造器org.apache.spark.mllib.regression.LinearRegressionModel
-
- LinearRegressionParams - org.apache.spark.ml.regression中的接口
-
Params for linear regression.
- LinearRegressionSummary - org.apache.spark.ml.regression中的类
-
Linear regression results evaluated on a dataset.
- LinearRegressionTrainingSummary - org.apache.spark.ml.regression中的类
-
Linear regression training results.
- LinearRegressionWithSGD - org.apache.spark.mllib.regression中的类
-
Train a linear regression model with no regularization using Stochastic Gradient Descent.
- LinearSVC - org.apache.spark.ml.classification中的类
-
- LinearSVC(String) - 类 的构造器org.apache.spark.ml.classification.LinearSVC
-
- LinearSVC() - 类 的构造器org.apache.spark.ml.classification.LinearSVC
-
- LinearSVCModel - org.apache.spark.ml.classification中的类
-
- LinearSVCParams - org.apache.spark.ml.classification中的接口
-
Params for linear SVM Classifier.
- link(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.CLogLog$
-
- link(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Identity$
-
- link(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Inverse$
-
- link() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
- link(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Log$
-
- link(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Logit$
-
- link(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Probit$
-
- link(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Sqrt$
-
- link() - 接口 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionBase
-
Param for the name of link function which provides the relationship
between the linear predictor and the mean of the distribution function.
- link() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
- Link$() - 类 的构造器org.apache.spark.ml.regression.GeneralizedLinearRegression.Link$
-
- linkPower() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
- linkPower() - 接口 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionBase
-
Param for the index in the power link function.
- linkPower() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
- linkPredictionCol() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
- linkPredictionCol() - 接口 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionBase
-
Param for link prediction (linear predictor) column name.
- linkPredictionCol() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
- listColumns(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Returns a list of columns for the given table/view or temporary view.
- listColumns(String, String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Returns a list of columns for the given table/view in the specified database.
- listDatabases() - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Returns a list of databases available across all sessions.
- listDatabases(String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
List the names of all the databases that match the specified pattern.
- listenerBus() - 接口 中的方法org.apache.spark.ml.MLEvents
-
- ListenerBus<L,E> - org.apache.spark.util中的接口
-
An event bus which posts events to its listeners.
- listenerManager() - 类 中的方法org.apache.spark.sql.SparkSession
-
- listenerManager() - 类 中的方法org.apache.spark.sql.SQLContext
-
- listeners() - 接口 中的方法org.apache.spark.util.ListenerBus
-
- listFiles() - 类 中的方法org.apache.spark.SparkContext
-
Returns a list of file paths that are added to resources.
- listFunctions() - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Returns a list of functions registered in the current database.
- listFunctions(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Returns a list of functions registered in the specified database.
- listFunctions(String, String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Return the names of all functions that match the given pattern in the database.
- listingTable(Seq<String>, Function1<T, Seq<Node>>, Iterable<T>, boolean, Option<String>, Seq<String>, boolean, boolean, Seq<Option<String>>) - 类 中的静态方法org.apache.spark.ui.UIUtils
-
Returns an HTML table constructed by generating a row for each object in a sequence.
- listJars() - 类 中的方法org.apache.spark.SparkContext
-
Returns a list of jar files that are added to resources.
- listListeners() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryManager
-
- listNamespaces() - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- listNamespaces(String[]) - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- listNamespaces() - 接口 中的方法org.apache.spark.sql.connector.catalog.SupportsNamespaces
-
List top-level namespaces from the catalog.
- listNamespaces(String[]) - 接口 中的方法org.apache.spark.sql.connector.catalog.SupportsNamespaces
-
List namespaces in a namespace.
- listOrcFiles(String, Configuration) - 类 中的静态方法org.apache.spark.sql.hive.orc.OrcFileOperator
-
- listResourceIds(SparkConf, String) - 类 中的静态方法org.apache.spark.resource.ResourceUtils
-
- listTables() - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Returns a list of tables/views in the current database.
- listTables(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Returns a list of tables/views in the specified database.
- listTables(String[]) - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- listTables(String[]) - 接口 中的方法org.apache.spark.sql.connector.catalog.TableCatalog
-
List the tables in a namespace from the catalog.
- listTables(String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Returns the names of all tables in the given database.
- listTables(String, String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Returns the names of tables in the given database that matches the given pattern.
- Lit - org.apache.spark.sql.connector.expressions中的类
-
Convenience extractor for any Literal.
- Lit() - 类 的构造器org.apache.spark.sql.connector.expressions.Lit
-
- lit(Object) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a
Column of literal value.
- literal(String) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- literal(T) - 类 中的静态方法org.apache.spark.sql.connector.expressions.Expressions
-
Create a literal from a value.
- Literal<T> - org.apache.spark.sql.connector.expressions中的接口
-
Represents a constant literal value in the public expression API.
- literal(T) - 类 中的静态方法org.apache.spark.sql.connector.expressions.LogicalExpressions
-
- literal(T, DataType) - 类 中的静态方法org.apache.spark.sql.connector.expressions.LogicalExpressions
-
- LIVE_ENTITY_UPDATE_MIN_FLUSH_PERIOD() - 类 中的静态方法org.apache.spark.internal.config.Status
-
- LIVE_ENTITY_UPDATE_PERIOD() - 类 中的静态方法org.apache.spark.internal.config.Status
-
- LiveEntityHelpers - org.apache.spark.status中的类
-
- LiveEntityHelpers() - 类 的构造器org.apache.spark.status.LiveEntityHelpers
-
- LiveExecutor - org.apache.spark.status中的类
-
- LiveExecutor(String, long) - 类 的构造器org.apache.spark.status.LiveExecutor
-
- LiveExecutorStageSummary - org.apache.spark.status中的类
-
- LiveExecutorStageSummary(int, int, String) - 类 的构造器org.apache.spark.status.LiveExecutorStageSummary
-
- LiveJob - org.apache.spark.status中的类
-
- LiveJob(int, String, Option<String>, Option<Date>, Seq<Object>, Option<String>, int, Option<Object>) - 类 的构造器org.apache.spark.status.LiveJob
-
- LiveRDD - org.apache.spark.status中的类
-
Tracker for data related to a persisted RDD.
- LiveRDD(RDDInfo, StorageLevel) - 类 的构造器org.apache.spark.status.LiveRDD
-
- LiveRDDDistribution - org.apache.spark.status中的类
-
- LiveRDDDistribution(LiveExecutor) - 类 的构造器org.apache.spark.status.LiveRDDDistribution
-
- LiveRDDPartition - org.apache.spark.status中的类
-
Data about a single partition of a cached RDD.
- LiveRDDPartition(String, StorageLevel) - 类 的构造器org.apache.spark.status.LiveRDDPartition
-
- LiveStage - org.apache.spark.status中的类
-
- LiveStage() - 类 的构造器org.apache.spark.status.LiveStage
-
- LiveTask - org.apache.spark.status中的类
-
- LiveTask(TaskInfo, int, int, Option<Object>) - 类 的构造器org.apache.spark.status.LiveTask
-
- load(String) - 类 中的静态方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- load(String) - 类 中的静态方法org.apache.spark.ml.classification.GBTClassificationModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.classification.GBTClassifier
-
- load(String) - 类 中的静态方法org.apache.spark.ml.classification.LinearSVC
-
- load(String) - 类 中的静态方法org.apache.spark.ml.classification.LinearSVCModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.classification.LogisticRegression
-
- load(String) - 类 中的静态方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
- load(String) - 类 中的静态方法org.apache.spark.ml.classification.NaiveBayes
-
- load(String) - 类 中的静态方法org.apache.spark.ml.classification.NaiveBayesModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.classification.OneVsRest
-
- load(String) - 类 中的静态方法org.apache.spark.ml.classification.OneVsRestModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.classification.RandomForestClassifier
-
- load(String) - 类 中的静态方法org.apache.spark.ml.clustering.BisectingKMeans
-
- load(String) - 类 中的静态方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.clustering.DistributedLDAModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.clustering.GaussianMixture
-
- load(String) - 类 中的静态方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.clustering.KMeans
-
- load(String) - 类 中的静态方法org.apache.spark.ml.clustering.KMeansModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.clustering.LDA
-
- load(String) - 类 中的静态方法org.apache.spark.ml.clustering.LocalLDAModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.clustering.PowerIterationClustering
-
- load(String) - 类 中的静态方法org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
-
- load(String) - 类 中的静态方法org.apache.spark.ml.evaluation.ClusteringEvaluator
-
- load(String) - 类 中的静态方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- load(String) - 类 中的静态方法org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator
-
- load(String) - 类 中的静态方法org.apache.spark.ml.evaluation.RankingEvaluator
-
- load(String) - 类 中的静态方法org.apache.spark.ml.evaluation.RegressionEvaluator
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.Binarizer
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.BucketedRandomProjectionLSH
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.BucketedRandomProjectionLSHModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.Bucketizer
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.ChiSqSelector
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.ChiSqSelectorModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.ColumnPruner
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.CountVectorizer
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.CountVectorizerModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.DCT
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.ElementwiseProduct
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.FeatureHasher
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.HashingTF
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.IDF
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.IDFModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.Imputer
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.ImputerModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.IndexToString
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.Interaction
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.MaxAbsScaler
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.MaxAbsScalerModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.MinHashLSH
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.MinHashLSHModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.MinMaxScaler
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.MinMaxScalerModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.NGram
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.Normalizer
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.OneHotEncoder
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.OneHotEncoderModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.PCA
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.PCAModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.PolynomialExpansion
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.QuantileDiscretizer
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.RegexTokenizer
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.RFormula
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.RobustScaler
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.RobustScalerModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.SQLTransformer
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.StandardScaler
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.StandardScalerModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.StopWordsRemover
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.StringIndexer
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.StringIndexerModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.Tokenizer
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.VectorAssembler
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.VectorAttributeRewriter
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.VectorIndexer
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.VectorIndexerModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.VectorSizeHint
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.VectorSlicer
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.Word2Vec
-
- load(String) - 类 中的静态方法org.apache.spark.ml.feature.Word2VecModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.fpm.FPGrowth
-
- load(String) - 类 中的静态方法org.apache.spark.ml.fpm.FPGrowthModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.Pipeline
-
- load(String, SparkContext, String) - 类 中的方法org.apache.spark.ml.Pipeline.SharedReadWrite$
-
- load(String) - 类 中的静态方法org.apache.spark.ml.PipelineModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.r.RWrappers
-
- load(String) - 类 中的静态方法org.apache.spark.ml.recommendation.ALS
-
- load(String) - 类 中的静态方法org.apache.spark.ml.recommendation.ALSModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- load(String) - 类 中的静态方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- load(String) - 类 中的静态方法org.apache.spark.ml.regression.GBTRegressionModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.regression.GBTRegressor
-
- load(String) - 类 中的静态方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
- load(String) - 类 中的静态方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.regression.IsotonicRegression
-
- load(String) - 类 中的静态方法org.apache.spark.ml.regression.IsotonicRegressionModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.regression.LinearRegression
-
- load(String) - 类 中的静态方法org.apache.spark.ml.regression.LinearRegressionModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.regression.RandomForestRegressor
-
- load(String) - 类 中的静态方法org.apache.spark.ml.tuning.CrossValidator
-
- load(String) - 类 中的静态方法org.apache.spark.ml.tuning.CrossValidatorModel
-
- load(String) - 类 中的静态方法org.apache.spark.ml.tuning.TrainValidationSplit
-
- load(String) - 类 中的静态方法org.apache.spark.ml.tuning.TrainValidationSplitModel
-
- load(String) - 接口 中的方法org.apache.spark.ml.util.MLReadable
-
Reads an ML instance from the input path, a shortcut of read.load(path).
- load(String) - 类 中的方法org.apache.spark.ml.util.MLReader
-
Loads the ML component from the input path.
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.classification.LogisticRegressionModel
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.classification.NaiveBayesModel
-
- load(SparkContext, String) - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV1_0$
-
- load(SparkContext, String) - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV2_0$
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.classification.SVMModel
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.clustering.BisectingKMeansModel
-
- load(SparkContext, String) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeansModel.SaveLoadV1_0$
-
- load(SparkContext, String) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeansModel.SaveLoadV2_0$
-
- load(SparkContext, String) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeansModel.SaveLoadV3_0$
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.clustering.DistributedLDAModel
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.clustering.GaussianMixtureModel
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.clustering.KMeansModel
-
- load(SparkContext, String) - 类 中的方法org.apache.spark.mllib.clustering.KMeansModel.SaveLoadV1_0$
-
- load(SparkContext, String) - 类 中的方法org.apache.spark.mllib.clustering.KMeansModel.SaveLoadV2_0$
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.clustering.LocalLDAModel
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.clustering.PowerIterationClusteringModel
-
- load(SparkContext, String) - 类 中的方法org.apache.spark.mllib.clustering.PowerIterationClusteringModel.SaveLoadV1_0$
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.feature.ChiSqSelectorModel
-
- load(SparkContext, String) - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelectorModel.SaveLoadV1_0$
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.feature.Word2VecModel
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.fpm.FPGrowthModel
-
- load(SparkContext, String) - 类 中的方法org.apache.spark.mllib.fpm.FPGrowthModel.SaveLoadV1_0$
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.fpm.PrefixSpanModel
-
- load(SparkContext, String) - 类 中的方法org.apache.spark.mllib.fpm.PrefixSpanModel.SaveLoadV1_0$
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.recommendation.MatrixFactorizationModel
-
Load a model from the given path.
- load(SparkContext, String) - 类 中的方法org.apache.spark.mllib.recommendation.MatrixFactorizationModel.SaveLoadV1_0$
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.regression.IsotonicRegressionModel
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.regression.LassoModel
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.regression.LinearRegressionModel
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.regression.RidgeRegressionModel
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.tree.model.DecisionTreeModel
-
- load(SparkContext, String, String, int) - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
-
- load(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.tree.model.RandomForestModel
-
- load(SparkContext, String) - 接口 中的方法org.apache.spark.mllib.util.Loader
-
Load a model from the given path.
- load(String, SQLConf) - 类 中的静态方法org.apache.spark.sql.connector.catalog.Catalogs
-
Load and configure a catalog by name.
- load(String...) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads input in as a DataFrame, for data sources that support multiple paths.
- load() - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads input in as a DataFrame, for data sources that don't require a path (e.g. external
key-value stores).
- load(String) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads input in as a DataFrame, for data sources that require a path (e.g. data backed by
a local or distributed file system).
- load(Seq<String>) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads input in as a DataFrame, for data sources that support multiple paths.
- load() - 类 中的方法org.apache.spark.sql.streaming.DataStreamReader
-
Loads input data stream in as a DataFrame, for data streams that don't require a path
(e.g. external key-value stores).
- load(String) - 类 中的方法org.apache.spark.sql.streaming.DataStreamReader
-
Loads input in as a DataFrame, for data streams that read from some path.
- loadClass(String, boolean) - 类 中的方法org.apache.spark.util.ChildFirstURLClassLoader
-
- loadClass(String, boolean) - 类 中的方法org.apache.spark.util.ParentClassLoader
-
- loadData(SparkContext, String, String) - 类 中的方法org.apache.spark.mllib.classification.impl.GLMClassificationModel.SaveLoadV1_0$
-
Helper method for loading GLM classification model data.
- loadData(SparkContext, String, String, int) - 类 中的方法org.apache.spark.mllib.regression.impl.GLMRegressionModel.SaveLoadV1_0$
-
Helper method for loading GLM regression model data.
- loadDefaultSparkProperties(SparkConf, String) - 类 中的静态方法org.apache.spark.util.Utils
-
Load default Spark properties from the given file.
- loadDefaultStopWords(String) - 类 中的静态方法org.apache.spark.ml.feature.StopWordsRemover
-
Loads the default stop words for the given language.
- loadDynamicPartitions(String, String, String, LinkedHashMap<String, String>, boolean, int) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Loads new dynamic partitions into an existing table.
- Loader<M extends Saveable> - org.apache.spark.mllib.util中的接口
-
:: DeveloperApi ::
Trait for classes which can load models and transformers from files.
- loadExtensions(Class<T>, Seq<String>, SparkConf) - 类 中的静态方法org.apache.spark.util.Utils
-
Create instances of extension classes.
- loadImpl(String, SparkSession, String, String) - 类 中的静态方法org.apache.spark.ml.tree.EnsembleModelReadWrite
-
Helper method for loading a tree ensemble from disk.
- loadImpl(Dataset<Row>, Item, ClassTag<Item>) - 类 中的方法org.apache.spark.mllib.fpm.FPGrowthModel.SaveLoadV1_0$
-
- loadImpl(Dataset<Row>, Item, ClassTag<Item>) - 类 中的方法org.apache.spark.mllib.fpm.PrefixSpanModel.SaveLoadV1_0$
-
- LoadInstanceEnd<T> - org.apache.spark.ml中的类
-
Event fired after MLReader.load.
- LoadInstanceEnd() - 类 的构造器org.apache.spark.ml.LoadInstanceEnd
-
- LoadInstanceStart<T> - org.apache.spark.ml中的类
-
Event fired before MLReader.load.
- LoadInstanceStart(String) - 类 的构造器org.apache.spark.ml.LoadInstanceStart
-
- loadLabeledPoints(SparkContext, String, int) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Loads labeled points saved using RDD[LabeledPoint].saveAsTextFile.
- loadLabeledPoints(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Loads labeled points saved using RDD[LabeledPoint].saveAsTextFile with the default number of
partitions.
- loadLibSVMFile(SparkContext, String, int, int) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Loads labeled data in the LIBSVM format into an RDD[LabeledPoint].
- loadLibSVMFile(SparkContext, String, int) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], with the default number of
partitions.
- loadLibSVMFile(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Loads binary labeled data in the LIBSVM format into an RDD[LabeledPoint], with number of
features determined automatically and the default number of partitions.
- loadNamespaceMetadata(String[]) - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- loadNamespaceMetadata(String[]) - 接口 中的方法org.apache.spark.sql.connector.catalog.SupportsNamespaces
-
Load metadata properties for a namespace.
- loadPartition(String, String, String, LinkedHashMap<String, String>, boolean, boolean, boolean) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Loads a static partition into an existing table.
- loadRelation(CatalogPlugin, Identifier) - 类 中的静态方法org.apache.spark.sql.connector.catalog.CatalogV2Util
-
- loadTable(CatalogPlugin, Identifier) - 类 中的静态方法org.apache.spark.sql.connector.catalog.CatalogV2Util
-
- loadTable(Identifier) - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- loadTable(Identifier) - 接口 中的方法org.apache.spark.sql.connector.catalog.TableCatalog
-
Load table metadata by
identifier from the catalog.
- loadTable(String, String, boolean, boolean) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Loads data into an existing table.
- loadTreeNodes(String, org.apache.spark.ml.util.DefaultParamsReader.Metadata, SparkSession) - 类 中的静态方法org.apache.spark.ml.tree.DecisionTreeModelReadWrite
-
Load a decision tree from a file.
- loadVectors(SparkContext, String, int) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Loads vectors saved using RDD[Vector].saveAsTextFile.
- loadVectors(SparkContext, String) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Loads vectors saved using RDD[Vector].saveAsTextFile with the default number of partitions.
- LOCAL_BLOCKS_FETCHED() - 类 中的方法org.apache.spark.InternalAccumulator.shuffleRead$
-
- LOCAL_BYTES_READ() - 类 中的方法org.apache.spark.InternalAccumulator.shuffleRead$
-
- LOCAL_CLUSTER_REGEX() - 类 中的静态方法org.apache.spark.SparkMasterRegex
-
- LOCAL_N_FAILURES_REGEX() - 类 中的静态方法org.apache.spark.SparkMasterRegex
-
- LOCAL_N_REGEX() - 类 中的静态方法org.apache.spark.SparkMasterRegex
-
- LOCAL_SCHEME() - 类 中的静态方法org.apache.spark.util.Utils
-
Scheme used for files that are locally available on worker nodes in the cluster.
- LOCAL_STORE_DIR() - 类 中的静态方法org.apache.spark.internal.config.History
-
- localBlocksFetched() - 类 中的方法org.apache.spark.status.api.v1.ShuffleReadMetricDistributions
-
- localBlocksFetched() - 类 中的方法org.apache.spark.status.api.v1.ShuffleReadMetrics
-
- localBytesRead() - 类 中的方法org.apache.spark.status.api.v1.ShuffleReadMetrics
-
- localCanonicalHostName() - 类 中的静态方法org.apache.spark.util.Utils
-
Get the local machine's FQDN.
- localCheckpoint() - 类 中的方法org.apache.spark.rdd.RDD
-
Mark this RDD for local checkpointing using Spark's existing caching layer.
- localCheckpoint() - 类 中的方法org.apache.spark.sql.Dataset
-
Eagerly locally checkpoints a Dataset and return the new Dataset.
- localCheckpoint(boolean) - 类 中的方法org.apache.spark.sql.Dataset
-
Locally checkpoints a Dataset and return the new Dataset.
- LOCALDATE() - 类 中的静态方法org.apache.spark.sql.Encoders
-
Creates an encoder that serializes instances of the java.time.LocalDate class
to the internal representation of nullable Catalyst's DateType.
- localDirs() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.BlockLocationsAndStatus
-
- localDirs() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.RegisterBlockManager
-
- locale() - 类 中的方法org.apache.spark.ml.feature.StopWordsRemover
-
Locale of the input for case insensitive matching.
- localHostName() - 类 中的静态方法org.apache.spark.util.Utils
-
Get the local machine's hostname.
- localHostNameForURI() - 类 中的静态方法org.apache.spark.util.Utils
-
Get the local machine's URI.
- LOCALITY() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- localityAwareTasks() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RequestExecutors
-
- localitySummary() - 类 中的方法org.apache.spark.status.LiveStage
-
- LocalKMeans - org.apache.spark.mllib.clustering中的类
-
An utility object to run K-means locally.
- LocalKMeans() - 类 的构造器org.apache.spark.mllib.clustering.LocalKMeans
-
- LocalLDAModel - org.apache.spark.ml.clustering中的类
-
Local (non-distributed) model fitted by
LDA.
- LocalLDAModel - org.apache.spark.mllib.clustering中的类
-
Local LDA model.
- localSeqToDatasetHolder(Seq<T>, Encoder<T>) - 类 中的方法org.apache.spark.sql.SQLImplicits
-
Creates a
Dataset from a local Seq.
- localSparkRPackagePath() - 类 中的静态方法org.apache.spark.api.r.RUtils
-
Get the SparkR package path in the local spark distribution.
- locate(String, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Locate the position of the first occurrence of substr.
- locate(String, Column, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Locate the position of the first occurrence of substr in a string column, after position pos.
- location() - 接口 中的方法org.apache.spark.scheduler.MapStatus
-
Location where this task was run.
- location() - 类 中的方法org.apache.spark.streaming.scheduler.ReceiverInfo
-
- location() - 类 中的方法org.apache.spark.ui.storage.ExecutorStreamSummary
-
- locations() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.BlockLocationsAndStatus
-
- locationUri() - 类 中的方法org.apache.spark.sql.catalog.Database
-
- log() - 接口 中的方法org.apache.spark.internal.Logging
-
- log(Function0<Parsers.Parser<T>>, String) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- log(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the natural logarithm of the given value.
- log(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the natural logarithm of the given column.
- log(double, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the first argument-base logarithm of the second argument.
- log(double, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the first argument-base logarithm of the second argument.
- Log$() - 类 的构造器org.apache.spark.ml.regression.GeneralizedLinearRegression.Log$
-
- log10(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the logarithm of the given value in base 10.
- log10(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the logarithm of the given value in base 10.
- log1p(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the natural logarithm of the given value plus one.
- log1p(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the natural logarithm of the given column plus one.
- log2(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the logarithm of the given column in base 2.
- log2(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the logarithm of the given value in base 2.
- logDebug(Function0<String>) - 接口 中的方法org.apache.spark.internal.Logging
-
- logDebug(Function0<String>, Throwable) - 接口 中的方法org.apache.spark.internal.Logging
-
- logDeprecationWarning(String) - 类 中的静态方法org.apache.spark.SparkConf
-
Logs a warning message if the given config key is deprecated.
- logError(Function0<String>) - 接口 中的方法org.apache.spark.internal.Logging
-
- logError(Function0<String>, Throwable) - 接口 中的方法org.apache.spark.internal.Logging
-
- logEvent() - 接口 中的方法org.apache.spark.ml.MLEvent
-
- logEvent(MLEvent) - 接口 中的方法org.apache.spark.ml.MLEvents
-
- logEvent() - 接口 中的方法org.apache.spark.scheduler.SparkListenerEvent
-
- Logging - org.apache.spark.internal中的接口
-
Utility trait for classes that want to log data.
- LogicalExpressions - org.apache.spark.sql.connector.expressions中的类
-
Helper methods for working with the logical expressions API.
- LogicalExpressions() - 类 的构造器org.apache.spark.sql.connector.expressions.LogicalExpressions
-
- logInfo(Function0<String>) - 接口 中的方法org.apache.spark.internal.Logging
-
- logInfo(Function0<String>, Throwable) - 接口 中的方法org.apache.spark.internal.Logging
-
- LogisticGradient - org.apache.spark.mllib.optimization中的类
-
:: DeveloperApi ::
Compute gradient and loss for a multinomial logistic loss function, as used
in multi-class classification (it is also used in binary logistic regression).
- LogisticGradient(int) - 类 的构造器org.apache.spark.mllib.optimization.LogisticGradient
-
- LogisticGradient() - 类 的构造器org.apache.spark.mllib.optimization.LogisticGradient
-
- LogisticRegression - org.apache.spark.ml.classification中的类
-
Logistic regression.
- LogisticRegression(String) - 类 的构造器org.apache.spark.ml.classification.LogisticRegression
-
- LogisticRegression() - 类 的构造器org.apache.spark.ml.classification.LogisticRegression
-
- LogisticRegressionDataGenerator - org.apache.spark.mllib.util中的类
-
:: DeveloperApi ::
Generate test data for LogisticRegression.
- LogisticRegressionDataGenerator() - 类 的构造器org.apache.spark.mllib.util.LogisticRegressionDataGenerator
-
- LogisticRegressionModel - org.apache.spark.ml.classification中的类
-
- LogisticRegressionModel - org.apache.spark.mllib.classification中的类
-
Classification model trained using Multinomial/Binary Logistic Regression.
- LogisticRegressionModel(Vector, double, int, int) - 类 的构造器org.apache.spark.mllib.classification.LogisticRegressionModel
-
- LogisticRegressionModel(Vector, double) - 类 的构造器org.apache.spark.mllib.classification.LogisticRegressionModel
-
- LogisticRegressionParams - org.apache.spark.ml.classification中的接口
-
Params for logistic regression.
- LogisticRegressionSummary - org.apache.spark.ml.classification中的接口
-
Abstraction for logistic regression results for a given model.
- LogisticRegressionSummaryImpl - org.apache.spark.ml.classification中的类
-
Multiclass logistic regression results for a given model.
- LogisticRegressionSummaryImpl(Dataset<Row>, String, String, String, String) - 类 的构造器org.apache.spark.ml.classification.LogisticRegressionSummaryImpl
-
- LogisticRegressionTrainingSummary - org.apache.spark.ml.classification中的接口
-
Abstraction for multiclass logistic regression training results.
- LogisticRegressionTrainingSummaryImpl - org.apache.spark.ml.classification中的类
-
Multiclass logistic regression training results.
- LogisticRegressionTrainingSummaryImpl(Dataset<Row>, String, String, String, String, double[]) - 类 的构造器org.apache.spark.ml.classification.LogisticRegressionTrainingSummaryImpl
-
- LogisticRegressionWithLBFGS - org.apache.spark.mllib.classification中的类
-
Train a classification model for Multinomial/Binary Logistic Regression using
Limited-memory BFGS.
- LogisticRegressionWithLBFGS() - 类 的构造器org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
-
- LogisticRegressionWithSGD - org.apache.spark.mllib.classification中的类
-
Train a classification model for Binary Logistic Regression
using Stochastic Gradient Descent.
- Logit$() - 类 的构造器org.apache.spark.ml.regression.GeneralizedLinearRegression.Logit$
-
- logLikelihood() - 类 中的方法org.apache.spark.ml.clustering.ExpectationAggregator
-
- logLikelihood() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureSummary
-
- logLikelihood(Dataset<?>) - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
Calculates a lower bound on the log likelihood of the entire corpus.
- logLikelihood() - 类 中的方法org.apache.spark.mllib.clustering.DistributedLDAModel
-
- logLikelihood() - 类 中的方法org.apache.spark.mllib.clustering.ExpectationSum
-
- logLikelihood(RDD<Tuple2<Object, Vector>>) - 类 中的方法org.apache.spark.mllib.clustering.LocalLDAModel
-
Calculates a lower bound on the log likelihood of the entire corpus.
- logLikelihood(JavaPairRDD<Long, Vector>) - 类 中的方法org.apache.spark.mllib.clustering.LocalLDAModel
-
Java-friendly version of logLikelihood
- logLoss(double) - 类 中的方法org.apache.spark.mllib.evaluation.MulticlassMetrics
-
Returns the log-loss, aka logistic loss or cross-entropy loss.
- LogLoss - org.apache.spark.mllib.tree.loss中的类
-
:: DeveloperApi ::
Class for log loss calculation (for classification).
- LogLoss() - 类 的构造器org.apache.spark.mllib.tree.loss.LogLoss
-
- logName() - 接口 中的方法org.apache.spark.internal.Logging
-
- LogNormalGenerator - org.apache.spark.mllib.random中的类
-
:: DeveloperApi ::
Generates i.i.d. samples from the log normal distribution with the
given mean and standard deviation.
- LogNormalGenerator(double, double) - 类 的构造器org.apache.spark.mllib.random.LogNormalGenerator
-
- logNormalGraph(SparkContext, int, int, double, double, long) - 类 中的静态方法org.apache.spark.graphx.util.GraphGenerators
-
Generate a graph whose vertex out degree distribution is log normal.
- logNormalJavaRDD(JavaSparkContext, double, double, long, int, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
Java-friendly version of RandomRDDs.logNormalRDD.
- logNormalJavaRDD(JavaSparkContext, double, double, long, int) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
RandomRDDs.logNormalJavaRDD with the default seed.
- logNormalJavaRDD(JavaSparkContext, double, double, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
RandomRDDs.logNormalJavaRDD with the default number of partitions and the default seed.
- logNormalJavaVectorRDD(JavaSparkContext, double, double, long, int, int, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
Java-friendly version of RandomRDDs.logNormalVectorRDD.
- logNormalJavaVectorRDD(JavaSparkContext, double, double, long, int, int) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
RandomRDDs.logNormalJavaVectorRDD with the default seed.
- logNormalJavaVectorRDD(JavaSparkContext, double, double, long, int) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
RandomRDDs.logNormalJavaVectorRDD with the default number of partitions and
the default seed.
- logNormalRDD(SparkContext, double, double, long, int, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
Generates an RDD comprised of i.i.d.
- logNormalVectorRDD(SparkContext, double, double, long, int, int, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
Generates an RDD[Vector] with vectors containing i.i.d.
- logpdf(Vector) - 类 中的方法org.apache.spark.ml.stat.distribution.MultivariateGaussian
-
Returns the log-density of this multivariate Gaussian at given point, x
- logpdf(Vector) - 类 中的方法org.apache.spark.mllib.stat.distribution.MultivariateGaussian
-
Returns the log-density of this multivariate Gaussian at given point, x
- logPerplexity(Dataset<?>) - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
Calculate an upper bound on perplexity.
- logPerplexity(RDD<Tuple2<Object, Vector>>) - 类 中的方法org.apache.spark.mllib.clustering.LocalLDAModel
-
Calculate an upper bound on perplexity.
- logPerplexity(JavaPairRDD<Long, Vector>) - 类 中的方法org.apache.spark.mllib.clustering.LocalLDAModel
-
Java-friendly version of logPerplexity
- logPrior() - 类 中的方法org.apache.spark.ml.clustering.DistributedLDAModel
-
- logPrior() - 类 中的方法org.apache.spark.mllib.clustering.DistributedLDAModel
-
- logResourceInfo(String, Map<String, ResourceInformation>) - 类 中的静态方法org.apache.spark.resource.ResourceUtils
-
- logStartFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- logStartToJson(SparkListenerLogStart) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- logTrace(Function0<String>) - 接口 中的方法org.apache.spark.internal.Logging
-
- logTrace(Function0<String>, Throwable) - 接口 中的方法org.apache.spark.internal.Logging
-
- logTuningParams(org.apache.spark.ml.util.Instrumentation) - 接口 中的方法org.apache.spark.ml.tuning.ValidatorParams
-
Instrumentation logging for tuning params including the inner estimator and evaluator info.
- logUncaughtExceptions(Function0<T>) - 类 中的静态方法org.apache.spark.util.Utils
-
Execute the given block, logging and re-throwing any uncaught exception.
- logUrlMap() - 类 中的方法org.apache.spark.scheduler.cluster.ExecutorInfo
-
- logUrls() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterExecutor
-
- logWarning(Function0<String>) - 接口 中的方法org.apache.spark.internal.Logging
-
- logWarning(Function0<String>, Throwable) - 接口 中的方法org.apache.spark.internal.Logging
-
- LONG() - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for nullable long type.
- longAccumulator() - 类 中的方法org.apache.spark.SparkContext
-
Create and register a long accumulator, which starts with 0 and accumulates inputs by add.
- longAccumulator(String) - 类 中的方法org.apache.spark.SparkContext
-
Create and register a long accumulator, which starts with 0 and accumulates inputs by add.
- LongAccumulator - org.apache.spark.util中的类
-
An
accumulator for computing sum, count, and average of 64-bit integers.
- LongAccumulator() - 类 的构造器org.apache.spark.util.LongAccumulator
-
- LongAccumulatorSource - org.apache.spark.metrics.source中的类
-
- LongAccumulatorSource() - 类 的构造器org.apache.spark.metrics.source.LongAccumulatorSource
-
- LongExactNumeric - org.apache.spark.sql.types中的类
-
- LongExactNumeric() - 类 的构造器org.apache.spark.sql.types.LongExactNumeric
-
- LongParam - org.apache.spark.ml.param中的类
-
:: DeveloperApi ::
Specialized version of Param[Long] for Java.
- LongParam(String, String, String, Function1<Object, Object>) - 类 的构造器org.apache.spark.ml.param.LongParam
-
- LongParam(String, String, String) - 类 的构造器org.apache.spark.ml.param.LongParam
-
- LongParam(Identifiable, String, String, Function1<Object, Object>) - 类 的构造器org.apache.spark.ml.param.LongParam
-
- LongParam(Identifiable, String, String) - 类 的构造器org.apache.spark.ml.param.LongParam
-
- LongType - 类 中的静态变量org.apache.spark.sql.types.DataTypes
-
Gets the LongType object.
- LongType - org.apache.spark.sql.types中的类
-
The data type representing Long values.
- LongType() - 类 的构造器org.apache.spark.sql.types.LongType
-
- lookup(K) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return the list of values in the RDD for key key.
- lookup(K) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Return the list of values in the RDD for key key.
- LookupCatalog - org.apache.spark.sql.connector.catalog中的接口
-
A trait to encapsulate catalog lookup function and helpful extractors.
- LookupCatalog.AsTableIdentifier - org.apache.spark.sql.connector.catalog中的类
-
Extract legacy table identifier from a multi-part identifier.
- LookupCatalog.AsTableIdentifier$ - org.apache.spark.sql.connector.catalog中的类
-
Extract legacy table identifier from a multi-part identifier.
- LookupCatalog.AsTemporaryViewIdentifier - org.apache.spark.sql.connector.catalog中的类
-
For temp views, extract a table identifier from a multi-part identifier if it has no catalog.
- LookupCatalog.AsTemporaryViewIdentifier$ - org.apache.spark.sql.connector.catalog中的类
-
For temp views, extract a table identifier from a multi-part identifier if it has no catalog.
- LookupCatalog.CatalogAndIdentifierParts - org.apache.spark.sql.connector.catalog中的类
-
Extract catalog and the rest name parts from a multi-part identifier.
- LookupCatalog.CatalogAndIdentifierParts$ - org.apache.spark.sql.connector.catalog中的类
-
Extract catalog and the rest name parts from a multi-part identifier.
- LookupCatalog.CatalogAndNamespace - org.apache.spark.sql.connector.catalog中的类
-
Extract catalog and namespace from a multi-part identifier with the current catalog if needed.
- LookupCatalog.CatalogAndNamespace$ - org.apache.spark.sql.connector.catalog中的类
-
Extract catalog and namespace from a multi-part identifier with the current catalog if needed.
- LookupCatalog.CatalogObjectIdentifier - org.apache.spark.sql.connector.catalog中的类
-
Extract catalog and identifier from a multi-part identifier with the current catalog if needed.
- LookupCatalog.CatalogObjectIdentifier$ - org.apache.spark.sql.connector.catalog中的类
-
Extract catalog and identifier from a multi-part identifier with the current catalog if needed.
- lookupRpcTimeout(SparkConf) - 类 中的静态方法org.apache.spark.util.RpcUtils
-
Returns the default Spark timeout to use for RPC remote endpoint lookup.
- loss(DenseMatrix<Object>, DenseMatrix<Object>, DenseMatrix<Object>) - 接口 中的方法org.apache.spark.ml.ann.LossFunction
-
Returns the value of loss function.
- loss() - 接口 中的方法org.apache.spark.ml.optim.aggregator.DifferentiableLossAggregator
-
The current loss value of this aggregator.
- loss() - 接口 中的方法org.apache.spark.ml.param.shared.HasLoss
-
Param for the loss function to be optimized.
- loss() - 类 中的方法org.apache.spark.ml.regression.AFTAggregator
-
- loss() - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
- loss() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionModel
-
- loss() - 接口 中的方法org.apache.spark.ml.regression.LinearRegressionParams
-
The loss function to be optimized.
- loss() - 类 中的方法org.apache.spark.mllib.tree.configuration.BoostingStrategy
-
- Loss - org.apache.spark.mllib.tree.loss中的接口
-
:: DeveloperApi ::
Trait for adding "pluggable" loss functions for the gradient boosting algorithm.
- Losses - org.apache.spark.mllib.tree.loss中的类
-
- Losses() - 类 的构造器org.apache.spark.mllib.tree.loss.Losses
-
- LossFunction - org.apache.spark.ml.ann中的接口
-
Trait for loss function
- LossReasonPending - org.apache.spark.scheduler中的类
-
A loss reason that means we don't yet know why the executor exited.
- LossReasonPending() - 类 的构造器org.apache.spark.scheduler.LossReasonPending
-
- lossSum() - 接口 中的方法org.apache.spark.ml.optim.aggregator.DifferentiableLossAggregator
-
- lossType() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- lossType() - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- lossType() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- lossType() - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- lossType() - 接口 中的方法org.apache.spark.ml.tree.GBTClassifierParams
-
Loss function which GBT tries to minimize.
- lossType() - 接口 中的方法org.apache.spark.ml.tree.GBTRegressorParams
-
Loss function which GBT tries to minimize.
- LOST() - 类 中的静态方法org.apache.spark.TaskState
-
- low() - 类 中的方法org.apache.spark.partial.BoundedDouble
-
- lower() - 类 中的方法org.apache.spark.ml.feature.RobustScaler
-
- lower() - 类 中的方法org.apache.spark.ml.feature.RobustScalerModel
-
- lower() - 接口 中的方法org.apache.spark.ml.feature.RobustScalerParams
-
Lower quantile to calculate quantile range, shared by all features
Default: 0.25
- lower(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Converts a string column to lower case.
- lowerBoundsOnCoefficients() - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
- lowerBoundsOnCoefficients() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- lowerBoundsOnCoefficients() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionParams
-
The lower bounds on coefficients if fitting under bound constrained optimization.
- lowerBoundsOnIntercepts() - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
- lowerBoundsOnIntercepts() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- lowerBoundsOnIntercepts() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionParams
-
The lower bounds on intercepts if fitting under bound constrained optimization.
- LowPrioritySQLImplicits - org.apache.spark.sql中的接口
-
Lower priority implicit methods for converting Scala objects into
Datasets.
- lpad(Column, int, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Left-pad the string column with pad to a length of len.
- LSHParams - org.apache.spark.ml.feature中的接口
-
Params for LSH.
- lt(double) - 类 中的静态方法org.apache.spark.ml.param.ParamValidators
-
Check if value is less than upperBound
- lt(Object) - 类 中的方法org.apache.spark.sql.Column
-
Less than.
- lt(T, T) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- lt(T, T) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- lt(double, double) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- lt(float, float) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- lt(T, T) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- lt(T, T) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- lt(T, T) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- ltEq(double) - 类 中的静态方法org.apache.spark.ml.param.ParamValidators
-
Check if value is less than or equal to upperBound
- lteq(T, T) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- lteq(T, T) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- lteq(double, double) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- lteq(float, float) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- lteq(T, T) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- lteq(T, T) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- lteq(T, T) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- ltrim(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Trim the spaces from left end for the specified string value.
- ltrim(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Trim the specified character string from left end for the specified string column.
- LZ4CompressionCodec - org.apache.spark.io中的类
-
- LZ4CompressionCodec(SparkConf) - 类 的构造器org.apache.spark.io.LZ4CompressionCodec
-
- LZFCompressionCodec - org.apache.spark.io中的类
-
- LZFCompressionCodec(SparkConf) - 类 的构造器org.apache.spark.io.LZFCompressionCodec
-
- main(String[]) - 类 中的静态方法org.apache.spark.ml.param.shared.SharedParamsCodeGen
-
- main(String[]) - 类 中的静态方法org.apache.spark.mllib.util.KMeansDataGenerator
-
- main(String[]) - 类 中的静态方法org.apache.spark.mllib.util.LinearDataGenerator
-
- main(String[]) - 类 中的静态方法org.apache.spark.mllib.util.LogisticRegressionDataGenerator
-
- main(String[]) - 类 中的静态方法org.apache.spark.mllib.util.MFDataGenerator
-
- main(String[]) - 类 中的静态方法org.apache.spark.mllib.util.SVMDataGenerator
-
- main(String[]) - 类 中的静态方法org.apache.spark.streaming.util.RawTextSender
-
- main(String[]) - 类 中的静态方法org.apache.spark.ui.UIWorkloadGenerator
-
- main(String[]) - 接口 中的方法org.apache.spark.util.CommandLineUtils
-
- majorMinorVersion(String) - 类 中的静态方法org.apache.spark.util.VersionUtils
-
Given a Spark version string, return the (major version number, minor version number).
- majorVersion(String) - 类 中的静态方法org.apache.spark.util.VersionUtils
-
Given a Spark version string, return the major version number.
- makeBinarySearch(Ordering<K>, ClassTag<K>) - 类 中的静态方法org.apache.spark.util.CollectionsUtils
-
- makeCopy() - 接口 中的方法org.apache.spark.sql.Encoder
-
- makeDescription(String, String, boolean) - 类 中的静态方法org.apache.spark.ui.UIUtils
-
Returns HTML rendering of a job or stage description.
- makeDriverRef(String, SparkConf, org.apache.spark.rpc.RpcEnv) - 类 中的静态方法org.apache.spark.util.RpcUtils
-
Retrieve a RpcEndpointRef which is located in the driver via its name.
- makeHref(boolean, String, String) - 类 中的静态方法org.apache.spark.ui.UIUtils
-
Return the correct Href after checking if master is running in the
reverse proxy mode or not.
- makeProgressBar(int, int, int, int, Map<String, Object>, int) - 类 中的静态方法org.apache.spark.ui.UIUtils
-
- makeRDD(Seq<T>, int, ClassTag<T>) - 类 中的方法org.apache.spark.SparkContext
-
Distribute a local Scala collection to form an RDD.
- makeRDD(Seq<Tuple2<T, Seq<String>>>, ClassTag<T>) - 类 中的方法org.apache.spark.SparkContext
-
Distribute a local Scala collection to form an RDD, with one or more
location preferences (hostnames of Spark nodes) for each object.
- makeRDDForPartitionedTable(Seq<Partition>) - 接口 中的方法org.apache.spark.sql.hive.TableReader
-
- makeRDDForTable(Table) - 接口 中的方法org.apache.spark.sql.hive.TableReader
-
- map(Function<T, R>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return a new RDD by applying a function to all elements of this RDD.
- map(Function1<Object, Object>) - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Map the values of this matrix using a function.
- map(Function1<Object, Object>) - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
Map the values of this matrix using a function.
- map(Function1<R, T>) - 类 中的方法org.apache.spark.partial.PartialResult
-
Transform this PartialResult into a PartialResult of type T.
- map(Function1<T, U>, ClassTag<U>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return a new RDD by applying a function to all elements of this RDD.
- map(DataType, DataType) - 类 中的方法org.apache.spark.sql.ColumnName
-
Creates a new StructField of type map.
- map(MapType) - 类 中的方法org.apache.spark.sql.ColumnName
-
- map(Function1<T, U>, Encoder<U>) - 类 中的方法org.apache.spark.sql.Dataset
-
(Scala-specific)
Returns a new Dataset that contains the result of applying func to each element.
- map(MapFunction<T, U>, Encoder<U>) - 类 中的方法org.apache.spark.sql.Dataset
-
(Java-specific)
Returns a new Dataset that contains the result of applying func to each element.
- map(Column...) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a new map column.
- map(Seq<Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a new map column.
- map(Function<T, U>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream by applying a function to all elements of this DStream.
- map(Function1<T, U>, ClassTag<U>) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream by applying a function to all elements of this DStream.
- map_concat(Column...) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the union of all the given maps.
- map_concat(Seq<Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the union of all the given maps.
- map_entries(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns an unordered array of all entries in the given map.
- map_filter(Column, Function2<Column, Column, Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns a map whose key-value pairs satisfy a predicate.
- map_from_arrays(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a new map column.
- map_from_entries(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns a map created from the given array of entries.
- map_keys(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns an unordered array containing the keys of the map.
- map_values(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns an unordered array containing the values of the map.
- map_zip_with(Column, Column, Function3<Column, Column, Column, Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Merge two given maps, key-wise into a single map using a function.
- mapAsSerializableJavaMap(Map<A, B>) - 类 中的静态方法org.apache.spark.api.java.JavaUtils
-
- mapEdgePartitions(Function2<Object, EdgePartition<ED, VD>, EdgePartition<ED2, VD2>>, ClassTag<ED2>, ClassTag<VD2>) - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
- mapEdges(Function1<Edge<ED>, ED2>, ClassTag<ED2>) - 类 中的方法org.apache.spark.graphx.Graph
-
Transforms each edge attribute in the graph using the map function.
- mapEdges(Function2<Object, Iterator<Edge<ED>>, Iterator<ED2>>, ClassTag<ED2>) - 类 中的方法org.apache.spark.graphx.Graph
-
Transforms each edge attribute using the map function, passing it a whole partition at a
time.
- mapEdges(Function2<Object, Iterator<Edge<ED>>, Iterator<ED2>>, ClassTag<ED2>) - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- mapFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
-------------------------------- *
Util JSON deserialization methods |
- MapFunction<T,U> - org.apache.spark.api.java.function中的接口
-
Base interface for a map function used in Dataset's map function.
- mapGroups(Function2<K, Iterator<V>, U>, Encoder<U>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
(Scala-specific)
Applies the given function to each group of data.
- mapGroups(MapGroupsFunction<K, V, U>, Encoder<U>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
(Java-specific)
Applies the given function to each group of data.
- MapGroupsFunction<K,V,R> - org.apache.spark.api.java.function中的接口
-
Base interface for a map function used in GroupedDataset's mapGroup function.
- mapGroupsWithState(Function3<K, Iterator<V>, GroupState<S>, U>, Encoder<S>, Encoder<U>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
(Scala-specific)
Applies the given function to each group of data, while maintaining a user-defined per-group
state.
- mapGroupsWithState(GroupStateTimeout, Function3<K, Iterator<V>, GroupState<S>, U>, Encoder<S>, Encoder<U>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
(Scala-specific)
Applies the given function to each group of data, while maintaining a user-defined per-group
state.
- mapGroupsWithState(MapGroupsWithStateFunction<K, V, S, U>, Encoder<S>, Encoder<U>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
(Java-specific)
Applies the given function to each group of data, while maintaining a user-defined per-group
state.
- mapGroupsWithState(MapGroupsWithStateFunction<K, V, S, U>, Encoder<S>, Encoder<U>, GroupStateTimeout) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
(Java-specific)
Applies the given function to each group of data, while maintaining a user-defined per-group
state.
- MapGroupsWithStateFunction<K,V,S,R> - org.apache.spark.api.java.function中的接口
-
- mapId() - 类 中的方法org.apache.spark.FetchFailed
-
- mapId() - 接口 中的方法org.apache.spark.scheduler.MapStatus
-
The unique ID of this shuffle map task, if spark.shuffle.useOldFetchProtocol enabled we use
partitionId of the task or taskContext.taskAttemptId is used.
- mapId() - 类 中的方法org.apache.spark.storage.ShuffleBlockBatchId
-
- mapId() - 类 中的方法org.apache.spark.storage.ShuffleBlockId
-
- mapId() - 类 中的方法org.apache.spark.storage.ShuffleDataBlockId
-
- mapId() - 类 中的方法org.apache.spark.storage.ShuffleIndexBlockId
-
- mapIndex() - 类 中的方法org.apache.spark.FetchFailed
-
- mapOutputTracker() - 类 中的方法org.apache.spark.SparkEnv
-
- MapOutputTrackerMessage - org.apache.spark中的接口
-
- mapPartitions(FlatMapFunction<Iterator<T>, U>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return a new RDD by applying a function to each partition of this RDD.
- mapPartitions(FlatMapFunction<Iterator<T>, U>, boolean) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return a new RDD by applying a function to each partition of this RDD.
- mapPartitions(Function1<Iterator<T>, Iterator<U>>, boolean, ClassTag<U>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return a new RDD by applying a function to each partition of this RDD.
- mapPartitions(Function1<Iterator<T>, Iterator<S>>, boolean, ClassTag<S>) - 类 中的方法org.apache.spark.rdd.RDDBarrier
-
:: Experimental ::
Returns a new RDD by applying a function to each partition of the wrapped RDD,
where tasks are launched together in a barrier stage.
- mapPartitions(Function1<Iterator<T>, Iterator<U>>, Encoder<U>) - 类 中的方法org.apache.spark.sql.Dataset
-
(Scala-specific)
Returns a new Dataset that contains the result of applying func to each partition.
- mapPartitions(MapPartitionsFunction<T, U>, Encoder<U>) - 类 中的方法org.apache.spark.sql.Dataset
-
(Java-specific)
Returns a new Dataset that contains the result of applying f to each partition.
- mapPartitions(FlatMapFunction<Iterator<T>, U>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs
of this DStream.
- mapPartitions(Function1<Iterator<T>, Iterator<U>>, boolean, ClassTag<U>) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs
of this DStream.
- MapPartitionsFunction<T,U> - org.apache.spark.api.java.function中的接口
-
Base interface for function used in Dataset's mapPartitions.
- mapPartitionsToDouble(DoubleFlatMapFunction<Iterator<T>>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return a new RDD by applying a function to each partition of this RDD.
- mapPartitionsToDouble(DoubleFlatMapFunction<Iterator<T>>, boolean) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return a new RDD by applying a function to each partition of this RDD.
- mapPartitionsToPair(PairFlatMapFunction<Iterator<T>, K2, V2>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return a new RDD by applying a function to each partition of this RDD.
- mapPartitionsToPair(PairFlatMapFunction<Iterator<T>, K2, V2>, boolean) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return a new RDD by applying a function to each partition of this RDD.
- mapPartitionsToPair(PairFlatMapFunction<Iterator<T>, K2, V2>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD is generated by applying mapPartitions() to each RDDs
of this DStream.
- mapPartitionsWithIndex(Function2<Integer, Iterator<T>, Iterator<R>>, boolean) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return a new RDD by applying a function to each partition of this RDD, while tracking the index
of the original partition.
- mapPartitionsWithIndex(Function2<Object, Iterator<T>, Iterator<U>>, boolean, ClassTag<U>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return a new RDD by applying a function to each partition of this RDD, while tracking the index
of the original partition.
- mapPartitionsWithIndex(Function2<Object, Iterator<T>, Iterator<S>>, boolean, ClassTag<S>) - 类 中的方法org.apache.spark.rdd.RDDBarrier
-
:: Experimental ::
Returns a new RDD by applying a function to each partition of the wrapped RDD, while tracking
the index of the original partition.
- mapPartitionsWithInputSplit(Function2<InputSplit, Iterator<Tuple2<K, V>>, Iterator<R>>, boolean) - 类 中的方法org.apache.spark.api.java.JavaHadoopRDD
-
Maps over a partition, providing the InputSplit that was used as the base of the partition.
- mapPartitionsWithInputSplit(Function2<InputSplit, Iterator<Tuple2<K, V>>, Iterator<R>>, boolean) - 类 中的方法org.apache.spark.api.java.JavaNewHadoopRDD
-
Maps over a partition, providing the InputSplit that was used as the base of the partition.
- mapPartitionsWithInputSplit(Function2<InputSplit, Iterator<Tuple2<K, V>>, Iterator<U>>, boolean, ClassTag<U>) - 类 中的方法org.apache.spark.rdd.HadoopRDD
-
Maps over a partition, providing the InputSplit that was used as the base of the partition.
- mapPartitionsWithInputSplit(Function2<InputSplit, Iterator<Tuple2<K, V>>, Iterator<U>>, boolean, ClassTag<U>) - 类 中的方法org.apache.spark.rdd.NewHadoopRDD
-
Maps over a partition, providing the InputSplit that was used as the base of the partition.
- MappedPoolMemory - org.apache.spark.metrics中的类
-
- MappedPoolMemory() - 类 的构造器org.apache.spark.metrics.MappedPoolMemory
-
- mapredInputFormat() - 类 中的方法org.apache.spark.scheduler.InputFormatInfo
-
- mapreduceInputFormat() - 类 中的方法org.apache.spark.scheduler.InputFormatInfo
-
- mapSideCombine() - 类 中的方法org.apache.spark.ShuffleDependency
-
- MapStatus - org.apache.spark.scheduler中的接口
-
Result returned by a ShuffleMapTask to a scheduler.
- mapStatuses() - 类 中的方法org.apache.spark.ShuffleStatus
-
MapStatus for each partition.
- mapToDouble(DoubleFunction<T>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return a new RDD by applying a function to all elements of this RDD.
- mapToJson(Map<String, String>) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
------------------------------ *
Util JSON serialization methods |
- mapToPair(PairFunction<T, K2, V2>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return a new RDD by applying a function to all elements of this RDD.
- mapToPair(PairFunction<T, K2, V2>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream by applying a function to all elements of this DStream.
- mapTriplets(Function1<EdgeTriplet<VD, ED>, ED2>, ClassTag<ED2>) - 类 中的方法org.apache.spark.graphx.Graph
-
Transforms each edge attribute using the map function, passing it the adjacent vertex
attributes as well.
- mapTriplets(Function1<EdgeTriplet<VD, ED>, ED2>, TripletFields, ClassTag<ED2>) - 类 中的方法org.apache.spark.graphx.Graph
-
Transforms each edge attribute using the map function, passing it the adjacent vertex
attributes as well.
- mapTriplets(Function2<Object, Iterator<EdgeTriplet<VD, ED>>, Iterator<ED2>>, TripletFields, ClassTag<ED2>) - 类 中的方法org.apache.spark.graphx.Graph
-
Transforms each edge attribute a partition at a time using the map function, passing it the
adjacent vertex attributes as well.
- mapTriplets(Function2<Object, Iterator<EdgeTriplet<VD, ED>>, Iterator<ED2>>, TripletFields, ClassTag<ED2>) - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- MapType - org.apache.spark.sql.types中的类
-
The data type for Maps.
- MapType(DataType, DataType, boolean) - 类 的构造器org.apache.spark.sql.types.MapType
-
- MapType() - 类 的构造器org.apache.spark.sql.types.MapType
-
No-arg constructor for kryo.
- mapValues(Function<V, U>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Pass each value in the key-value pair RDD through a map function without changing the keys;
this also retains the original RDD's partitioning.
- mapValues(Function1<Edge<ED>, ED2>, ClassTag<ED2>) - 类 中的方法org.apache.spark.graphx.EdgeRDD
-
Map the values in an edge partitioning preserving the structure but changing the values.
- mapValues(Function1<Edge<ED>, ED2>, ClassTag<ED2>) - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
- mapValues(Function1<VD, VD2>, ClassTag<VD2>) - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- mapValues(Function2<Object, VD, VD2>, ClassTag<VD2>) - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- mapValues(Function1<VD, VD2>, ClassTag<VD2>) - 类 中的方法org.apache.spark.graphx.VertexRDD
-
Maps each vertex attribute, preserving the index.
- mapValues(Function2<Object, VD, VD2>, ClassTag<VD2>) - 类 中的方法org.apache.spark.graphx.VertexRDD
-
Maps each vertex attribute, additionally supplying the vertex ID.
- mapValues(Function1<V, U>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Pass each value in the key-value pair RDD through a map function without changing the keys;
this also retains the original RDD's partitioning.
- mapValues(Function1<V, W>, Encoder<W>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
- mapValues(MapFunction<V, W>, Encoder<W>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
- mapValues(Function<V, U>) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying a map function to the value of each key-value pairs in
'this' DStream without changing the key.
- mapValues(Function1<V, U>, ClassTag<U>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying a map function to the value of each key-value pairs in
'this' DStream without changing the key.
- mapVertices(Function2<Object, VD, VD2>, ClassTag<VD2>, Predef.$eq$colon$eq<VD, VD2>) - 类 中的方法org.apache.spark.graphx.Graph
-
Transforms each vertex attribute in the graph using the map function.
- mapVertices(Function2<Object, VD, VD2>, ClassTag<VD2>, Predef.$eq$colon$eq<VD, VD2>) - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- mapWithState(StateSpec<K, V, StateType, MappedType>) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a
JavaMapWithStateDStream by applying a function to every key-value element of
this stream, while maintaining some state data for each unique key.
- mapWithState(StateSpec<K, V, StateType, MappedType>, ClassTag<StateType>, ClassTag<MappedType>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a
MapWithStateDStream by applying a function to every key-value element of
this stream, while maintaining some state data for each unique key.
- MapWithStateDStream<KeyType,ValueType,StateType,MappedType> - org.apache.spark.streaming.dstream中的类
-
DStream representing the stream of data generated by
mapWithState operation on a
pair DStream.
- MapWithStateDStream(StreamingContext, ClassTag<MappedType>) - 类 的构造器org.apache.spark.streaming.dstream.MapWithStateDStream
-
- mark(int) - 类 中的方法org.apache.spark.storage.BufferReleasingInputStream
-
- markSupported() - 类 中的方法org.apache.spark.storage.BufferReleasingInputStream
-
- mask(Graph<VD2, ED2>, ClassTag<VD2>, ClassTag<ED2>) - 类 中的方法org.apache.spark.graphx.Graph
-
Restricts the graph to only the vertices and edges that are also in other, but keeps the
attributes from this graph.
- mask(Graph<VD2, ED2>, ClassTag<VD2>, ClassTag<ED2>) - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- master() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
- MASTER() - 类 中的静态方法org.apache.spark.metrics.MetricsSystemInstances
-
- master() - 类 中的方法org.apache.spark.SparkContext
-
- master(String) - 类 中的方法org.apache.spark.sql.SparkSession.Builder
-
Sets the Spark master URL to connect to, such as "local" to run locally, "local[4]" to
run locally with 4 cores, or "spark://master:7077" to run on a Spark standalone cluster.
- Matrices - org.apache.spark.ml.linalg中的类
-
- Matrices() - 类 的构造器org.apache.spark.ml.linalg.Matrices
-
- Matrices - org.apache.spark.mllib.linalg中的类
-
- Matrices() - 类 的构造器org.apache.spark.mllib.linalg.Matrices
-
- Matrix - org.apache.spark.ml.linalg中的接口
-
Trait for a local matrix.
- Matrix - org.apache.spark.mllib.linalg中的接口
-
Trait for a local matrix.
- MatrixEntry - org.apache.spark.mllib.linalg.distributed中的类
-
Represents an entry in a distributed matrix.
- MatrixEntry(long, long, double) - 类 的构造器org.apache.spark.mllib.linalg.distributed.MatrixEntry
-
- MatrixFactorizationModel - org.apache.spark.mllib.recommendation中的类
-
Model representing the result of matrix factorization.
- MatrixFactorizationModel(int, RDD<Tuple2<Object, double[]>>, RDD<Tuple2<Object, double[]>>) - 类 的构造器org.apache.spark.mllib.recommendation.MatrixFactorizationModel
-
- MatrixFactorizationModel.SaveLoadV1_0$ - org.apache.spark.mllib.recommendation中的类
-
- MatrixImplicits - org.apache.spark.mllib.linalg中的类
-
Implicit methods available in Scala for converting
Matrix to
Matrix and vice versa.
- MatrixImplicits() - 类 的构造器org.apache.spark.mllib.linalg.MatrixImplicits
-
- MatrixType() - 类 中的静态方法org.apache.spark.ml.linalg.SQLDataTypes
-
- max() - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Returns the maximum element from this RDD as defined by
the default comparator natural order.
- max(Comparator<T>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Returns the maximum element from this RDD as defined by the specified
Comparator[T].
- MAX() - 类 中的静态方法org.apache.spark.ml.attribute.AttributeKeys
-
- max() - 类 中的方法org.apache.spark.ml.attribute.NumericAttribute
-
- max() - 类 中的方法org.apache.spark.ml.feature.MinMaxScaler
-
- max() - 类 中的方法org.apache.spark.ml.feature.MinMaxScalerModel
-
- max() - 接口 中的方法org.apache.spark.ml.feature.MinMaxScalerParams
-
upper bound after transformation, shared by all features
Default: 1.0
- max(Column, Column) - 类 中的静态方法org.apache.spark.ml.stat.Summarizer
-
- max(Column) - 类 中的静态方法org.apache.spark.ml.stat.Summarizer
-
- max() - 类 中的方法org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
-
Maximum value of each dimension.
- max() - 接口 中的方法org.apache.spark.mllib.stat.MultivariateStatisticalSummary
-
Maximum value of each column.
- max(Ordering<T>) - 类 中的方法org.apache.spark.rdd.RDD
-
Returns the max of this RDD as defined by the implicit Ordering[T].
- max(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the maximum value of the expression in a group.
- max(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the maximum value of the column in a group.
- max(String...) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Compute the max value for each numeric columns for each group.
- max(Seq<String>) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Compute the max value for each numeric columns for each group.
- max(T, T) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- max(T, T) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- max(double, double) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- max(float, float) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- max(T, T) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- max(T, T) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- max(T, T) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- max(Duration) - 类 中的方法org.apache.spark.streaming.Duration
-
- max(Time) - 类 中的方法org.apache.spark.streaming.Time
-
- max(long, long) - 类 中的静态方法org.apache.spark.streaming.util.RawTextHelper
-
- max() - 类 中的方法org.apache.spark.util.StatCounter
-
- MAX_DRIVER_LOG_AGE_S() - 类 中的静态方法org.apache.spark.internal.config.History
-
- MAX_EXECUTOR_RETRIES() - 类 中的静态方法org.apache.spark.internal.config.Deploy
-
- MAX_FEATURES_FOR_NORMAL_SOLVER() - 类 中的静态方法org.apache.spark.ml.regression.LinearRegression
-
When using LinearRegression.solver == "normal", the solver must limit the number of
features to at most this number.
- MAX_INT_DIGITS() - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
Maximum number of decimal digits an Int can represent
- MAX_LOCAL_DISK_USAGE() - 类 中的静态方法org.apache.spark.internal.config.History
-
- MAX_LOG_AGE_S() - 类 中的静态方法org.apache.spark.internal.config.History
-
- MAX_LOG_NUM() - 类 中的静态方法org.apache.spark.internal.config.History
-
- MAX_LONG_DIGITS() - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
Maximum number of decimal digits a Long can represent
- MAX_PRECISION() - 类 中的静态方法org.apache.spark.sql.types.DecimalType
-
- MAX_RETAINED_DEAD_EXECUTORS() - 类 中的静态方法org.apache.spark.internal.config.Status
-
- MAX_RETAINED_JOBS() - 类 中的静态方法org.apache.spark.internal.config.Status
-
- MAX_RETAINED_ROOT_NODES() - 类 中的静态方法org.apache.spark.internal.config.Status
-
- MAX_RETAINED_STAGES() - 类 中的静态方法org.apache.spark.internal.config.Status
-
- MAX_RETAINED_TASKS_PER_STAGE() - 类 中的静态方法org.apache.spark.internal.config.Status
-
- MAX_SCALE() - 类 中的静态方法org.apache.spark.sql.types.DecimalType
-
- maxAbs() - 类 中的方法org.apache.spark.ml.feature.MaxAbsScalerModel
-
- MaxAbsScaler - org.apache.spark.ml.feature中的类
-
Rescale each feature individually to range [-1, 1] by dividing through the largest maximum
absolute value in each feature.
- MaxAbsScaler(String) - 类 的构造器org.apache.spark.ml.feature.MaxAbsScaler
-
- MaxAbsScaler() - 类 的构造器org.apache.spark.ml.feature.MaxAbsScaler
-
- MaxAbsScalerModel - org.apache.spark.ml.feature中的类
-
- MaxAbsScalerParams - org.apache.spark.ml.feature中的接口
-
- maxBins() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- maxBins() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- maxBins() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- maxBins() - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- maxBins() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- maxBins() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- maxBins() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- maxBins() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- maxBins() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- maxBins() - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- maxBins() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- maxBins() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- maxBins() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
Maximum number of bins used for discretizing continuous features and for choosing how to split
on features at each node.
- maxBins() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- maxBufferSizeMb() - 类 中的方法org.apache.spark.serializer.KryoSerializer
-
- maxCategories() - 类 中的方法org.apache.spark.ml.feature.VectorIndexer
-
- maxCategories() - 类 中的方法org.apache.spark.ml.feature.VectorIndexerModel
-
- maxCategories() - 接口 中的方法org.apache.spark.ml.feature.VectorIndexerParams
-
Threshold for the number of values a categorical feature can take.
- maxCores() - 类 中的方法org.apache.spark.status.api.v1.ApplicationInfo
-
- maxDepth() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- maxDepth() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- maxDepth() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- maxDepth() - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- maxDepth() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- maxDepth() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- maxDepth() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- maxDepth() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- maxDepth() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- maxDepth() - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- maxDepth() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- maxDepth() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- maxDepth() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
Maximum depth of the tree (nonnegative).
- maxDepth() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- maxDF() - 类 中的方法org.apache.spark.ml.feature.CountVectorizer
-
- maxDF() - 类 中的方法org.apache.spark.ml.feature.CountVectorizerModel
-
- maxDF() - 接口 中的方法org.apache.spark.ml.feature.CountVectorizerParams
-
Specifies the maximum number of different documents a term could appear in to be included
in the vocabulary.
- maxId() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.Algo
-
- maxId() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.EnsembleCombiningStrategy
-
- maxId() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.FeatureType
-
- maxId() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.QuantileStrategy
-
- maxId() - 类 中的静态方法org.apache.spark.rdd.CheckpointState
-
- maxId() - 类 中的静态方法org.apache.spark.rdd.DeterministicLevel
-
- maxId() - 类 中的静态方法org.apache.spark.scheduler.SchedulingMode
-
- maxId() - 类 中的静态方法org.apache.spark.scheduler.TaskLocality
-
- maxId() - 类 中的静态方法org.apache.spark.streaming.scheduler.ReceiverState
-
- maxId() - 类 中的静态方法org.apache.spark.TaskState
-
- maxIter() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- maxIter() - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- maxIter() - 类 中的方法org.apache.spark.ml.classification.LinearSVC
-
- maxIter() - 类 中的方法org.apache.spark.ml.classification.LinearSVCModel
-
- maxIter() - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
- maxIter() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- maxIter() - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
- maxIter() - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeans
-
- maxIter() - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
- maxIter() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixture
-
- maxIter() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- maxIter() - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- maxIter() - 类 中的方法org.apache.spark.ml.clustering.KMeansModel
-
- maxIter() - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- maxIter() - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
- maxIter() - 类 中的方法org.apache.spark.ml.clustering.PowerIterationClustering
-
- maxIter() - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- maxIter() - 类 中的方法org.apache.spark.ml.feature.Word2VecModel
-
- maxIter() - 接口 中的方法org.apache.spark.ml.param.shared.HasMaxIter
-
Param for maximum number of iterations (>= 0).
- maxIter() - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- maxIter() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- maxIter() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- maxIter() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- maxIter() - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- maxIter() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
- maxIter() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
- maxIter() - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
- maxIter() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionModel
-
- maxIters() - 类 中的方法org.apache.spark.graphx.lib.SVDPlusPlus.Conf
-
- maxLocalProjDBSize() - 类 中的方法org.apache.spark.ml.fpm.PrefixSpan
-
Param for the maximum number of items (including delimiters used in the internal storage
format) allowed in a projected database before local processing (default: 32000000).
- maxMem() - 类 中的方法org.apache.spark.scheduler.SparkListenerBlockManagerAdded
-
- maxMemory() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- maxMemory() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- maxMemoryInMB() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- maxMemoryInMB() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- maxMemoryInMB() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- maxMemoryInMB() - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- maxMemoryInMB() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- maxMemoryInMB() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- maxMemoryInMB() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- maxMemoryInMB() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- maxMemoryInMB() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- maxMemoryInMB() - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- maxMemoryInMB() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- maxMemoryInMB() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- maxMemoryInMB() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
Maximum memory in MB allocated to histogram aggregation.
- maxMemoryInMB() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- maxMessageSizeBytes(SparkConf) - 类 中的静态方法org.apache.spark.util.RpcUtils
-
Returns the configured max message size for messages in bytes.
- maxNodesInLevel(int) - 类 中的静态方法org.apache.spark.mllib.tree.model.Node
-
Return the maximum number of nodes which can be in the given level of the tree.
- maxNumConcurrentTasks() - 接口 中的方法org.apache.spark.scheduler.SchedulerBackend
-
Get the max number of tasks that can be concurrent launched currently.
- maxOffHeapMem() - 类 中的方法org.apache.spark.scheduler.SparkListenerBlockManagerAdded
-
- maxOffHeapMemSize() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.RegisterBlockManager
-
- maxOnHeapMem() - 类 中的方法org.apache.spark.scheduler.SparkListenerBlockManagerAdded
-
- maxOnHeapMemSize() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.RegisterBlockManager
-
- maxPatternLength() - 类 中的方法org.apache.spark.ml.fpm.PrefixSpan
-
Param for the maximal pattern length (default: 10).
- maxPrecisionForBytes(int) - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- maxReplicas() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.ReplicateBlock
-
- maxSentenceLength() - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- maxSentenceLength() - 接口 中的方法org.apache.spark.ml.feature.Word2VecBase
-
Sets the maximum length (in words) of each sentence in the input data.
- maxSentenceLength() - 类 中的方法org.apache.spark.ml.feature.Word2VecModel
-
- maxSplitFeatureIndex() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeModel
-
Trace down the tree, and return the largest feature index used in any split.
- maxTasks() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- maxTasks() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- maxVal() - 类 中的方法org.apache.spark.graphx.lib.SVDPlusPlus.Conf
-
- maybeUpdateOutputMetrics(OutputMetrics, Function0<Object>, long) - 类 中的静态方法org.apache.spark.internal.io.SparkHadoopWriterUtils
-
- md5(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Calculates the MD5 digest of a binary column and returns the value
as a 32 character hex string.
- mean() - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Compute the mean of this RDD's elements.
- mean() - 类 中的方法org.apache.spark.ml.feature.StandardScalerModel
-
- mean() - 类 中的方法org.apache.spark.ml.stat.distribution.MultivariateGaussian
-
- mean(Column, Column) - 类 中的静态方法org.apache.spark.ml.stat.Summarizer
-
- mean(Column) - 类 中的静态方法org.apache.spark.ml.stat.Summarizer
-
- mean() - 类 中的方法org.apache.spark.mllib.feature.StandardScalerModel
-
- mean() - 类 中的方法org.apache.spark.mllib.random.ExponentialGenerator
-
- mean() - 类 中的方法org.apache.spark.mllib.random.LogNormalGenerator
-
- mean() - 类 中的方法org.apache.spark.mllib.random.PoissonGenerator
-
- mean() - 类 中的方法org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
-
Sample mean of each dimension.
- mean() - 接口 中的方法org.apache.spark.mllib.stat.MultivariateStatisticalSummary
-
Sample mean vector.
- mean() - 类 中的方法org.apache.spark.partial.BoundedDouble
-
- mean() - 类 中的方法org.apache.spark.rdd.DoubleRDDFunctions
-
Compute the mean of this RDD's elements.
- mean(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the average of the values in a group.
- mean(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the average of the values in a group.
- mean(String...) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Compute the average value for each numeric columns for each group.
- mean(Seq<String>) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Compute the average value for each numeric columns for each group.
- mean() - 类 中的方法org.apache.spark.util.StatCounter
-
- meanAbsoluteError() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionSummary
-
Returns the mean absolute error, which is a risk function corresponding to the
expected value of the absolute error loss or l1-norm loss.
- meanAbsoluteError() - 类 中的方法org.apache.spark.mllib.evaluation.RegressionMetrics
-
Returns the mean absolute error, which is a risk function corresponding to the
expected value of the absolute error loss or l1-norm loss.
- meanApprox(long, Double) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Return the approximate mean of the elements in this RDD.
- meanApprox(long) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Approximate operation to return the mean within a timeout.
- meanApprox(long, double) - 类 中的方法org.apache.spark.rdd.DoubleRDDFunctions
-
Approximate operation to return the mean within a timeout.
- meanAveragePrecision() - 类 中的方法org.apache.spark.mllib.evaluation.RankingMetrics
-
- meanAveragePrecisionAt(int) - 类 中的方法org.apache.spark.mllib.evaluation.RankingMetrics
-
Returns the mean average precision (MAP) at ranking position k of all the queries.
- means() - 类 中的方法org.apache.spark.ml.clustering.ExpectationAggregator
-
- means() - 类 中的方法org.apache.spark.mllib.clustering.ExpectationSum
-
- meanSquaredError() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionSummary
-
Returns the mean squared error, which is a risk function corresponding to the
expected value of the squared error loss or quadratic loss.
- meanSquaredError() - 类 中的方法org.apache.spark.mllib.evaluation.RegressionMetrics
-
Returns the mean squared error, which is a risk function corresponding to the
expected value of the squared error loss or quadratic loss.
- median() - 类 中的方法org.apache.spark.ml.feature.RobustScalerModel
-
- megabytesToString(long) - 类 中的静态方法org.apache.spark.util.Utils
-
Convert a quantity in megabytes to a human-readable string such as "4.0 MiB".
- MEM_SPILL() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- MEMORY_AND_DISK - 类 中的静态变量org.apache.spark.api.java.StorageLevels
-
- MEMORY_AND_DISK() - 类 中的静态方法org.apache.spark.storage.StorageLevel
-
- MEMORY_AND_DISK_2 - 类 中的静态变量org.apache.spark.api.java.StorageLevels
-
- MEMORY_AND_DISK_2() - 类 中的静态方法org.apache.spark.storage.StorageLevel
-
- MEMORY_AND_DISK_SER - 类 中的静态变量org.apache.spark.api.java.StorageLevels
-
- MEMORY_AND_DISK_SER() - 类 中的静态方法org.apache.spark.storage.StorageLevel
-
- MEMORY_AND_DISK_SER_2 - 类 中的静态变量org.apache.spark.api.java.StorageLevels
-
- MEMORY_AND_DISK_SER_2() - 类 中的静态方法org.apache.spark.storage.StorageLevel
-
- MEMORY_BYTES_SPILLED() - 类 中的静态方法org.apache.spark.InternalAccumulator
-
- MEMORY_ONLY - 类 中的静态变量org.apache.spark.api.java.StorageLevels
-
- MEMORY_ONLY() - 类 中的静态方法org.apache.spark.storage.StorageLevel
-
- MEMORY_ONLY_2 - 类 中的静态变量org.apache.spark.api.java.StorageLevels
-
- MEMORY_ONLY_2() - 类 中的静态方法org.apache.spark.storage.StorageLevel
-
- MEMORY_ONLY_SER - 类 中的静态变量org.apache.spark.api.java.StorageLevels
-
- MEMORY_ONLY_SER() - 类 中的静态方法org.apache.spark.storage.StorageLevel
-
- MEMORY_ONLY_SER_2 - 类 中的静态变量org.apache.spark.api.java.StorageLevels
-
- MEMORY_ONLY_SER_2() - 类 中的静态方法org.apache.spark.storage.StorageLevel
-
- memoryBytesSpilled() - 类 中的方法org.apache.spark.status.api.v1.ExecutorStageSummary
-
- memoryBytesSpilled() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- memoryBytesSpilled() - 类 中的方法org.apache.spark.status.api.v1.TaskMetricDistributions
-
- memoryBytesSpilled() - 类 中的方法org.apache.spark.status.api.v1.TaskMetrics
-
- memoryCost(int, int) - 类 中的静态方法org.apache.spark.mllib.feature.PCAUtil
-
- MemoryEntry<T> - org.apache.spark.storage.memory中的接口
-
- MemoryEntryBuilder<T> - org.apache.spark.storage.memory中的接口
-
- memoryManager() - 类 中的方法org.apache.spark.SparkEnv
-
- memoryMetrics() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- MemoryMetrics - org.apache.spark.status.api.v1中的类
-
- memoryMode() - 类 中的方法org.apache.spark.storage.memory.DeserializedMemoryEntry
-
- memoryMode() - 接口 中的方法org.apache.spark.storage.memory.MemoryEntry
-
- memoryMode() - 类 中的方法org.apache.spark.storage.memory.SerializedMemoryEntry
-
- MemoryParam - org.apache.spark.util中的类
-
An extractor object for parsing JVM memory strings, such as "10g", into an Int representing
the number of megabytes.
- MemoryParam() - 类 的构造器org.apache.spark.util.MemoryParam
-
- memoryPerExecutorMB() - 类 中的方法org.apache.spark.status.api.v1.ApplicationInfo
-
- memoryRemaining() - 类 中的方法org.apache.spark.status.api.v1.RDDDataDistribution
-
- memoryStringToMb(String) - 类 中的静态方法org.apache.spark.util.Utils
-
Convert a Java memory parameter passed to -Xmx (such as 300m or 1g) to a number of mebibytes.
- memoryUsed() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- memoryUsed() - 类 中的方法org.apache.spark.status.api.v1.RDDDataDistribution
-
- memoryUsed() - 类 中的方法org.apache.spark.status.api.v1.RDDPartitionInfo
-
- memoryUsed() - 类 中的方法org.apache.spark.status.api.v1.RDDStorageInfo
-
- memoryUsed() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- memoryUsed() - 类 中的方法org.apache.spark.status.LiveRDD
-
- memoryUsed() - 类 中的方法org.apache.spark.status.LiveRDDDistribution
-
- memoryUsed() - 类 中的方法org.apache.spark.status.LiveRDDPartition
-
- memoryUsedBytes() - 类 中的方法org.apache.spark.sql.streaming.StateOperatorProgress
-
- memSize() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.UpdateBlockInfo
-
- memSize() - 类 中的方法org.apache.spark.storage.BlockStatus
-
- memSize() - 类 中的方法org.apache.spark.storage.BlockUpdatedInfo
-
- memSize() - 类 中的方法org.apache.spark.storage.RDDInfo
-
- merge(ExpectationAggregator) - 类 中的方法org.apache.spark.ml.clustering.ExpectationAggregator
-
Merge another ExpectationAggregator, update the weights, means and covariances
for each distributions, and update the log likelihood.
- merge(OpenHashMap<String, Object>[], OpenHashMap<String, Object>[]) - 类 中的方法org.apache.spark.ml.feature.StringIndexerAggregator
-
- merge(Agg) - 接口 中的方法org.apache.spark.ml.optim.aggregator.DifferentiableLossAggregator
-
Merge two aggregators.
- merge(AFTAggregator) - 类 中的方法org.apache.spark.ml.regression.AFTAggregator
-
Merge another AFTAggregator, and update the loss and gradient
of the objective function.
- merge(IDF.DocumentFrequencyAggregator) - 类 中的方法org.apache.spark.mllib.feature.IDF.DocumentFrequencyAggregator
-
Merges another.
- merge(MultivariateOnlineSummarizer) - 类 中的方法org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
-
Merge another MultivariateOnlineSummarizer, and update the statistical summary.
- merge(int, U) - 接口 中的方法org.apache.spark.partial.ApproximateEvaluator
-
- merge(BUF, BUF) - 类 中的方法org.apache.spark.sql.expressions.Aggregator
-
Merge two intermediate values.
- merge(MutableAggregationBuffer, Row) - 类 中的方法org.apache.spark.sql.expressions.UserDefinedAggregateFunction
-
Merges two aggregation buffers and stores the updated buffer values back to buffer1.
- merge(AccumulatorV2<IN, OUT>) - 类 中的方法org.apache.spark.util.AccumulatorV2
-
Merges another same-type accumulator into this one and update its state, i.e. this should be
merge-in-place.
- merge(AccumulatorV2<T, List<T>>) - 类 中的方法org.apache.spark.util.CollectionAccumulator
-
- merge(AccumulatorV2<Double, Double>) - 类 中的方法org.apache.spark.util.DoubleAccumulator
-
- merge(AccumulatorV2<Long, Long>) - 类 中的方法org.apache.spark.util.LongAccumulator
-
- merge(double) - 类 中的方法org.apache.spark.util.StatCounter
-
Add a value into this StatCounter, updating the internal statistics.
- merge(TraversableOnce<Object>) - 类 中的方法org.apache.spark.util.StatCounter
-
Add multiple values into this StatCounter, updating the internal statistics.
- merge(StatCounter) - 类 中的方法org.apache.spark.util.StatCounter
-
Merge another StatCounter into this one, adding up the internal statistics.
- mergeCombiners() - 类 中的方法org.apache.spark.Aggregator
-
- mergeInPlace(BloomFilter) - 类 中的方法org.apache.spark.util.sketch.BloomFilter
-
Combines this bloom filter with another bloom filter by performing a bitwise OR of the
underlying data.
- mergeInPlace(CountMinSketch) - 类 中的方法org.apache.spark.util.sketch.CountMinSketch
-
- mergeOffsets(PartitionOffset[]) - 接口 中的方法org.apache.spark.sql.connector.read.streaming.ContinuousStream
-
- mergeValue() - 类 中的方法org.apache.spark.Aggregator
-
- MESOS_CLUSTER() - 类 中的静态方法org.apache.spark.metrics.MetricsSystemInstances
-
- message() - 类 中的方法org.apache.spark.FetchFailed
-
- message() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterExecutorFailed
-
- message() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RemoveWorker
-
- message() - 类 中的静态方法org.apache.spark.scheduler.ExecutorKilled
-
- message() - 类 中的静态方法org.apache.spark.scheduler.LossReasonPending
-
- message() - 异常错误 中的方法org.apache.spark.sql.AnalysisException
-
- message() - 异常错误 中的方法org.apache.spark.sql.streaming.StreamingQueryException
-
- message() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryStatus
-
- MessageLoop - org.apache.spark.rpc.netty中的类
-
A message loop used by Dispatcher to deliver messages to endpoints.
- MessageLoop(Dispatcher) - 类 的构造器org.apache.spark.rpc.netty.MessageLoop
-
- MetaAlgorithmReadWrite - org.apache.spark.ml.util中的类
-
Default Meta-Algorithm read and write implementation.
- MetaAlgorithmReadWrite() - 类 的构造器org.apache.spark.ml.util.MetaAlgorithmReadWrite
-
- Metadata - org.apache.spark.sql.types中的类
-
Metadata is a wrapper over Map[String, Any] that limits the value type to simple ones: Boolean,
Long, Double, String, Metadata, Array[Boolean], Array[Long], Array[Double], Array[String], and
Array[Metadata].
- metadata() - 类 中的方法org.apache.spark.sql.types.StructField
-
- metadata() - 类 中的方法org.apache.spark.streaming.scheduler.StreamInputInfo
-
- METADATA_KEY_DESCRIPTION() - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamInputInfo
-
The key for description in StreamInputInfo.metadata.
- MetadataBuilder - org.apache.spark.sql.types中的类
-
- MetadataBuilder() - 类 的构造器org.apache.spark.sql.types.MetadataBuilder
-
- metadataDescription() - 类 中的方法org.apache.spark.streaming.scheduler.StreamInputInfo
-
- MetadataUtils - org.apache.spark.ml.util中的类
-
Helper utilities for algorithms using ML metadata
- MetadataUtils() - 类 的构造器org.apache.spark.ml.util.MetadataUtils
-
- Method(String, Function2<Object, Object, Object>) - 类 的构造器org.apache.spark.mllib.stat.test.ChiSqTest.Method
-
- method() - 类 中的方法org.apache.spark.mllib.stat.test.ChiSqTestResult
-
- Method$() - 类 的构造器org.apache.spark.mllib.stat.test.ChiSqTest.Method$
-
- MethodIdentifier<T> - org.apache.spark.util中的类
-
Helper class to identify a method.
- MethodIdentifier(Class<T>, String, String) - 类 的构造器org.apache.spark.util.MethodIdentifier
-
- methodName() - 接口 中的方法org.apache.spark.mllib.stat.test.StreamingTestMethod
-
- methodName() - 类 中的静态方法org.apache.spark.mllib.stat.test.StudentTTest
-
- methodName() - 类 中的静态方法org.apache.spark.mllib.stat.test.WelchTTest
-
- METRIC_COMPILATION_TIME() - 类 中的静态方法org.apache.spark.metrics.source.CodegenMetrics
-
Histogram of the time it took to compile source code text (in milliseconds).
- METRIC_FILE_CACHE_HITS() - 类 中的静态方法org.apache.spark.metrics.source.HiveCatalogMetrics
-
Tracks the total number of files served from the file status cache instead of discovered.
- METRIC_FILES_DISCOVERED() - 类 中的静态方法org.apache.spark.metrics.source.HiveCatalogMetrics
-
Tracks the total number of files discovered off of the filesystem by InMemoryFileIndex.
- METRIC_GENERATED_CLASS_BYTECODE_SIZE() - 类 中的静态方法org.apache.spark.metrics.source.CodegenMetrics
-
Histogram of the bytecode size of each class generated by CodeGenerator.
- METRIC_GENERATED_METHOD_BYTECODE_SIZE() - 类 中的静态方法org.apache.spark.metrics.source.CodegenMetrics
-
Histogram of the bytecode size of each method in classes generated by CodeGenerator.
- METRIC_HIVE_CLIENT_CALLS() - 类 中的静态方法org.apache.spark.metrics.source.HiveCatalogMetrics
-
Tracks the total number of Hive client calls (e.g. to lookup a table).
- METRIC_PARALLEL_LISTING_JOB_COUNT() - 类 中的静态方法org.apache.spark.metrics.source.HiveCatalogMetrics
-
Tracks the total number of Spark jobs launched for parallel file listing.
- METRIC_PARTITIONS_FETCHED() - 类 中的静态方法org.apache.spark.metrics.source.HiveCatalogMetrics
-
Tracks the total number of partition metadata entries fetched via the client api.
- METRIC_SOURCE_CODE_SIZE() - 类 中的静态方法org.apache.spark.metrics.source.CodegenMetrics
-
Histogram of the length of source code text compiled by CodeGenerator (in characters).
- metricLabel() - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- metricLabel() - 类 中的方法org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator
-
- metricName() - 类 中的方法org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
-
param for metric name in evaluation (supports "areaUnderROC" (default), "areaUnderPR")
- metricName() - 类 中的方法org.apache.spark.ml.evaluation.ClusteringEvaluator
-
param for metric name in evaluation
(supports "silhouette" (default))
- metricName() - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
param for metric name in evaluation (supports "f1" (default), "accuracy",
"weightedPrecision", "weightedRecall", "weightedTruePositiveRate",
"weightedFalsePositiveRate", "weightedFMeasure", "truePositiveRateByLabel",
"falsePositiveRateByLabel", "precisionByLabel", "recallByLabel",
"fMeasureByLabel", "logLoss")
- metricName() - 类 中的方法org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator
-
param for metric name in evaluation (supports "f1Measure" (default), "subsetAccuracy",
"accuracy", "hammingLoss", "precision", "recall", "precisionByLabel",
"recallByLabel", "f1MeasureByLabel", "microPrecision", "microRecall",
"microF1Measure")
- metricName() - 类 中的方法org.apache.spark.ml.evaluation.RankingEvaluator
-
param for metric name in evaluation (supports "meanAveragePrecision" (default),
"meanAveragePrecisionAtK", "precisionAtK", "ndcgAtK", "recallAtK")
- metricName() - 类 中的方法org.apache.spark.ml.evaluation.RegressionEvaluator
-
Param for metric name in evaluation.
- metricPeaks() - 类 中的方法org.apache.spark.TaskKilled
-
- metricRegistry - 类 中的变量org.apache.spark.ExecutorPluginContext
-
- metricRegistry() - 类 中的静态方法org.apache.spark.metrics.source.CodegenMetrics
-
- metricRegistry() - 类 中的静态方法org.apache.spark.metrics.source.HiveCatalogMetrics
-
- metricRegistry() - 接口 中的方法org.apache.spark.metrics.source.Source
-
- metrics(String...) - 类 中的静态方法org.apache.spark.ml.stat.Summarizer
-
Given a list of metrics, provides a builder that it turns computes metrics from a column.
- metrics(Seq<String>) - 类 中的静态方法org.apache.spark.ml.stat.Summarizer
-
Given a list of metrics, provides a builder that it turns computes metrics from a column.
- metrics() - 类 中的方法org.apache.spark.sql.hive.execution.CreateHiveTableAsSelectCommand
-
- metrics() - 类 中的方法org.apache.spark.sql.hive.execution.InsertIntoHiveDirCommand
-
- metrics() - 类 中的方法org.apache.spark.sql.hive.execution.InsertIntoHiveTable
-
- metrics() - 类 中的方法org.apache.spark.sql.hive.execution.OptimizedCreateHiveTableAsSelectCommand
-
- metrics() - 类 中的方法org.apache.spark.status.LiveExecutorStageSummary
-
- metrics() - 类 中的方法org.apache.spark.status.LiveStage
-
- METRICS_PREFIX() - 类 中的静态方法org.apache.spark.InternalAccumulator
-
- metricsSystem() - 类 中的方法org.apache.spark.SparkEnv
-
- MetricsSystemInstances - org.apache.spark.metrics中的类
-
- MetricsSystemInstances() - 类 的构造器org.apache.spark.metrics.MetricsSystemInstances
-
- MFDataGenerator - org.apache.spark.mllib.util中的类
-
:: DeveloperApi ::
Generate RDD(s) containing data for Matrix Factorization.
- MFDataGenerator() - 类 的构造器org.apache.spark.mllib.util.MFDataGenerator
-
- MicroBatchStream - org.apache.spark.sql.connector.read.streaming中的接口
-
- microF1Measure() - 类 中的方法org.apache.spark.mllib.evaluation.MultilabelMetrics
-
- microPrecision() - 类 中的方法org.apache.spark.mllib.evaluation.MultilabelMetrics
-
- microRecall() - 类 中的方法org.apache.spark.mllib.evaluation.MultilabelMetrics
-
- mightContain(Object) - 类 中的方法org.apache.spark.util.sketch.BloomFilter
-
Returns true if the element might have been put in this Bloom filter,
false if this is definitely not the case.
- mightContainBinary(byte[]) - 类 中的方法org.apache.spark.util.sketch.BloomFilter
-
- mightContainLong(long) - 类 中的方法org.apache.spark.util.sketch.BloomFilter
-
- mightContainString(String) - 类 中的方法org.apache.spark.util.sketch.BloomFilter
-
- milliseconds() - 类 中的方法org.apache.spark.streaming.Duration
-
- milliseconds(long) - 类 中的静态方法org.apache.spark.streaming.Durations
-
- Milliseconds - org.apache.spark.streaming中的类
-
Helper object that creates instance of
Duration representing
a given number of milliseconds.
- Milliseconds() - 类 的构造器org.apache.spark.streaming.Milliseconds
-
- milliseconds() - 类 中的方法org.apache.spark.streaming.Time
-
- millisToString(long) - 类 中的静态方法org.apache.spark.scheduler.StatsReportListener
-
Reformat a time interval in milliseconds to a prettier format for output
- min() - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Returns the minimum element from this RDD as defined by
the default comparator natural order.
- min(Comparator<T>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Returns the minimum element from this RDD as defined by the specified
Comparator[T].
- MIN() - 类 中的静态方法org.apache.spark.ml.attribute.AttributeKeys
-
- min() - 类 中的方法org.apache.spark.ml.attribute.NumericAttribute
-
- min() - 类 中的方法org.apache.spark.ml.feature.MinMaxScaler
-
- min() - 类 中的方法org.apache.spark.ml.feature.MinMaxScalerModel
-
- min() - 接口 中的方法org.apache.spark.ml.feature.MinMaxScalerParams
-
lower bound after transformation, shared by all features
Default: 0.0
- min(Column, Column) - 类 中的静态方法org.apache.spark.ml.stat.Summarizer
-
- min(Column) - 类 中的静态方法org.apache.spark.ml.stat.Summarizer
-
- min() - 类 中的方法org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
-
Minimum value of each dimension.
- min() - 接口 中的方法org.apache.spark.mllib.stat.MultivariateStatisticalSummary
-
Minimum value of each column.
- min(Ordering<T>) - 类 中的方法org.apache.spark.rdd.RDD
-
Returns the min of this RDD as defined by the implicit Ordering[T].
- min(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the minimum value of the expression in a group.
- min(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the minimum value of the column in a group.
- min(String...) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Compute the min value for each numeric column for each group.
- min(Seq<String>) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Compute the min value for each numeric column for each group.
- min(T, T) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- min(T, T) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- min(double, double) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- min(float, float) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- min(T, T) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- min(T, T) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- min(T, T) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- min(Duration) - 类 中的方法org.apache.spark.streaming.Duration
-
- min(Time) - 类 中的方法org.apache.spark.streaming.Time
-
- min() - 类 中的方法org.apache.spark.util.StatCounter
-
- minBytesForPrecision() - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- minConfidence() - 类 中的方法org.apache.spark.ml.fpm.FPGrowth
-
- minConfidence() - 类 中的方法org.apache.spark.ml.fpm.FPGrowthModel
-
- minConfidence() - 接口 中的方法org.apache.spark.ml.fpm.FPGrowthParams
-
Minimal confidence for generating Association Rule. minConfidence will not affect the mining
for frequent itemsets, but will affect the association rules generation.
- minCount() - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- minCount() - 接口 中的方法org.apache.spark.ml.feature.Word2VecBase
-
The minimum number of times a token must appear to be included in the word2vec model's
vocabulary.
- minCount() - 类 中的方法org.apache.spark.ml.feature.Word2VecModel
-
- minDF() - 类 中的方法org.apache.spark.ml.feature.CountVectorizer
-
- minDF() - 类 中的方法org.apache.spark.ml.feature.CountVectorizerModel
-
- minDF() - 接口 中的方法org.apache.spark.ml.feature.CountVectorizerParams
-
Specifies the minimum number of different documents a term must appear in to be included
in the vocabulary.
- minDivisibleClusterSize() - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeans
-
- minDivisibleClusterSize() - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
- minDivisibleClusterSize() - 接口 中的方法org.apache.spark.ml.clustering.BisectingKMeansParams
-
The minimum number of points (if greater than or equal to 1.0) or the minimum proportion
of points (if less than 1.0) of a divisible cluster (default: 1.0).
- minDocFreq() - 类 中的方法org.apache.spark.ml.feature.IDF
-
- minDocFreq() - 接口 中的方法org.apache.spark.ml.feature.IDFBase
-
The minimum number of documents in which a term should appear.
- minDocFreq() - 类 中的方法org.apache.spark.ml.feature.IDFModel
-
- minDocFreq() - 类 中的方法org.apache.spark.mllib.feature.IDF.DocumentFrequencyAggregator
-
- minDocFreq() - 类 中的方法org.apache.spark.mllib.feature.IDF
-
- MinHashLSH - org.apache.spark.ml.feature中的类
-
LSH class for Jaccard distance.
- MinHashLSH(String) - 类 的构造器org.apache.spark.ml.feature.MinHashLSH
-
- MinHashLSH() - 类 的构造器org.apache.spark.ml.feature.MinHashLSH
-
- MinHashLSHModel - org.apache.spark.ml.feature中的类
-
Model produced by
MinHashLSH, where multiple hash functions are stored.
- MINIMUM_ADJUSTED_SCALE() - 类 中的静态方法org.apache.spark.sql.types.DecimalType
-
- minInfoGain() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- minInfoGain() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- minInfoGain() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- minInfoGain() - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- minInfoGain() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- minInfoGain() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- minInfoGain() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- minInfoGain() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- minInfoGain() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- minInfoGain() - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- minInfoGain() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- minInfoGain() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- minInfoGain() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
Minimum information gain for a split to be considered at a tree node.
- minInfoGain() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- minInstancesPerNode() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- minInstancesPerNode() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- minInstancesPerNode() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- minInstancesPerNode() - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- minInstancesPerNode() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- minInstancesPerNode() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- minInstancesPerNode() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- minInstancesPerNode() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- minInstancesPerNode() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- minInstancesPerNode() - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- minInstancesPerNode() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- minInstancesPerNode() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- minInstancesPerNode() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
Minimum number of instances each child must have after split.
- minInstancesPerNode() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- MinMax() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.QuantileStrategy
-
- MinMaxScaler - org.apache.spark.ml.feature中的类
-
Rescale each feature individually to a common range [min, max] linearly using column summary
statistics, which is also known as min-max normalization or Rescaling.
- MinMaxScaler(String) - 类 的构造器org.apache.spark.ml.feature.MinMaxScaler
-
- MinMaxScaler() - 类 的构造器org.apache.spark.ml.feature.MinMaxScaler
-
- MinMaxScalerModel - org.apache.spark.ml.feature中的类
-
- MinMaxScalerParams - org.apache.spark.ml.feature中的接口
-
- minorVersion(String) - 类 中的静态方法org.apache.spark.util.VersionUtils
-
Given a Spark version string, return the minor version number.
- minSamplingRate() - 类 中的静态方法org.apache.spark.util.random.BinomialBounds
-
- minShare() - 接口 中的方法org.apache.spark.scheduler.Schedulable
-
- minSupport() - 类 中的方法org.apache.spark.ml.fpm.FPGrowth
-
- minSupport() - 类 中的方法org.apache.spark.ml.fpm.FPGrowthModel
-
- minSupport() - 接口 中的方法org.apache.spark.ml.fpm.FPGrowthParams
-
Minimal support level of the frequent pattern. [0.0, 1.0].
- minSupport() - 类 中的方法org.apache.spark.ml.fpm.PrefixSpan
-
Param for the minimal support level (default: 0.1).
- minTF() - 类 中的方法org.apache.spark.ml.feature.CountVectorizer
-
- minTF() - 类 中的方法org.apache.spark.ml.feature.CountVectorizerModel
-
- minTF() - 接口 中的方法org.apache.spark.ml.feature.CountVectorizerParams
-
Filter to ignore rare words in a document.
- minTokenLength() - 类 中的方法org.apache.spark.ml.feature.RegexTokenizer
-
Minimum token length, greater than or equal to 0.
- minus(RDD<Tuple2<Object, VD>>) - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- minus(VertexRDD<VD>) - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- minus(RDD<Tuple2<Object, VD>>) - 类 中的方法org.apache.spark.graphx.VertexRDD
-
For each VertexId present in both this and other, minus will act as a set difference
operation returning only those unique VertexId's present in this.
- minus(VertexRDD<VD>) - 类 中的方法org.apache.spark.graphx.VertexRDD
-
For each VertexId present in both this and other, minus will act as a set difference
operation returning only those unique VertexId's present in this.
- minus(Object) - 类 中的方法org.apache.spark.sql.Column
-
Subtraction.
- minus(byte, byte) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- minus(Decimal, Decimal) - 接口 中的方法org.apache.spark.sql.types.Decimal.DecimalIsConflicted
-
- minus(Decimal, Decimal) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- minus(double, double) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- minus(float, float) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- minus(int, int) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- minus(long, long) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- minus(short, short) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- minus(Duration) - 类 中的方法org.apache.spark.streaming.Duration
-
- minus(Time) - 类 中的方法org.apache.spark.streaming.Time
-
- minus(Duration) - 类 中的方法org.apache.spark.streaming.Time
-
- minute(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Extracts the minutes as an integer from a given date/timestamp/string.
- minutes() - 类 中的静态方法org.apache.spark.scheduler.StatsReportListener
-
- minutes(long) - 类 中的静态方法org.apache.spark.streaming.Durations
-
- Minutes - org.apache.spark.streaming中的类
-
Helper object that creates instance of
Duration representing
a given number of minutes.
- Minutes() - 类 的构造器org.apache.spark.streaming.Minutes
-
- minVal() - 类 中的方法org.apache.spark.graphx.lib.SVDPlusPlus.Conf
-
- minWeightFractionPerNode() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- minWeightFractionPerNode() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- minWeightFractionPerNode() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- minWeightFractionPerNode() - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- minWeightFractionPerNode() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- minWeightFractionPerNode() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- minWeightFractionPerNode() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- minWeightFractionPerNode() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- minWeightFractionPerNode() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- minWeightFractionPerNode() - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- minWeightFractionPerNode() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- minWeightFractionPerNode() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- minWeightFractionPerNode() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
Minimum fraction of the weighted sample count that each child must have after split.
- minWeightFractionPerNode() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- missingValue() - 类 中的方法org.apache.spark.ml.feature.Imputer
-
- missingValue() - 类 中的方法org.apache.spark.ml.feature.ImputerModel
-
- missingValue() - 接口 中的方法org.apache.spark.ml.feature.ImputerParams
-
The placeholder for the missing values.
- mkList() - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- mkNumericOps(T) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- mkNumericOps(T) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- mkNumericOps(T) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- mkNumericOps(T) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- mkNumericOps(T) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- mkNumericOps(T) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- mkNumericOps(T) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- mkOrderingOps(T) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- mkOrderingOps(T) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- mkOrderingOps(T) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- mkOrderingOps(T) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- mkOrderingOps(T) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- mkOrderingOps(T) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- mkOrderingOps(T) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- mkString() - 接口 中的方法org.apache.spark.sql.Row
-
Displays all elements of this sequence in a string (without a separator).
- mkString(String) - 接口 中的方法org.apache.spark.sql.Row
-
Displays all elements of this sequence in a string using a separator string.
- mkString(String, String, String) - 接口 中的方法org.apache.spark.sql.Row
-
Displays all elements of this traversable or iterator in a string using
start, end, and separator strings.
- mkString(String, String, String) - 类 中的方法org.apache.spark.status.api.v1.StackTrace
-
- ML_ATTR() - 类 中的静态方法org.apache.spark.ml.attribute.AttributeKeys
-
- mlDenseMatrixToMLlibDenseMatrix(DenseMatrix) - 类 中的静态方法org.apache.spark.mllib.linalg.MatrixImplicits
-
- mlDenseVectorToMLlibDenseVector(DenseVector) - 类 中的静态方法org.apache.spark.mllib.linalg.VectorImplicits
-
- MLEvent - org.apache.spark.ml中的接口
-
Event emitted by ML operations.
- MLEvents - org.apache.spark.ml中的接口
-
A small trait that defines some methods to send
MLEvent.
- MLFormatRegister - org.apache.spark.ml.util中的接口
-
ML export formats for should implement this trait so that users can specify a shortname rather
than the fully qualified class name of the exporter.
- mllibDenseMatrixToMLDenseMatrix(DenseMatrix) - 类 中的静态方法org.apache.spark.mllib.linalg.MatrixImplicits
-
- mllibDenseVectorToMLDenseVector(DenseVector) - 类 中的静态方法org.apache.spark.mllib.linalg.VectorImplicits
-
- mllibMatrixToMLMatrix(Matrix) - 类 中的静态方法org.apache.spark.mllib.linalg.MatrixImplicits
-
- mllibSparseMatrixToMLSparseMatrix(SparseMatrix) - 类 中的静态方法org.apache.spark.mllib.linalg.MatrixImplicits
-
- mllibSparseVectorToMLSparseVector(SparseVector) - 类 中的静态方法org.apache.spark.mllib.linalg.VectorImplicits
-
- mllibVectorToMLVector(Vector) - 类 中的静态方法org.apache.spark.mllib.linalg.VectorImplicits
-
- mlMatrixToMLlibMatrix(Matrix) - 类 中的静态方法org.apache.spark.mllib.linalg.MatrixImplicits
-
- MLPairRDDFunctions<K,V> - org.apache.spark.mllib.rdd中的类
-
:: DeveloperApi ::
Machine learning specific Pair RDD functions.
- MLPairRDDFunctions(RDD<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>) - 类 的构造器org.apache.spark.mllib.rdd.MLPairRDDFunctions
-
- MLReadable<T> - org.apache.spark.ml.util中的接口
-
Trait for objects that provide MLReader.
- MLReader<T> - org.apache.spark.ml.util中的类
-
Abstract class for utility classes that can load ML instances.
- MLReader() - 类 的构造器org.apache.spark.ml.util.MLReader
-
- mlSparseMatrixToMLlibSparseMatrix(SparseMatrix) - 类 中的静态方法org.apache.spark.mllib.linalg.MatrixImplicits
-
- mlSparseVectorToMLlibSparseVector(SparseVector) - 类 中的静态方法org.apache.spark.mllib.linalg.VectorImplicits
-
- MLUtils - org.apache.spark.mllib.util中的类
-
Helper methods to load, save and pre-process data used in MLLib.
- MLUtils() - 类 的构造器org.apache.spark.mllib.util.MLUtils
-
- mlVectorToMLlibVector(Vector) - 类 中的静态方法org.apache.spark.mllib.linalg.VectorImplicits
-
- MLWritable - org.apache.spark.ml.util中的接口
-
Trait for classes that provide MLWriter.
- MLWriter - org.apache.spark.ml.util中的类
-
Abstract class for utility classes that can save ML instances in Spark's internal format.
- MLWriter() - 类 的构造器org.apache.spark.ml.util.MLWriter
-
- MLWriterFormat - org.apache.spark.ml.util中的接口
-
Abstract class to be implemented by objects that provide ML exportability.
- mod(Object) - 类 中的方法org.apache.spark.sql.Column
-
Modulo (a.k.a. remainder) expression.
- mode(SaveMode) - 类 中的方法org.apache.spark.sql.DataFrameWriter
-
Specifies the behavior when data or table already exists.
- mode(String) - 类 中的方法org.apache.spark.sql.DataFrameWriter
-
Specifies the behavior when data or table already exists.
- mode() - 接口 中的方法org.apache.spark.sql.hive.execution.CreateHiveTableAsSelectBase
-
- mode() - 类 中的方法org.apache.spark.sql.hive.execution.CreateHiveTableAsSelectCommand
-
- mode() - 类 中的方法org.apache.spark.sql.hive.execution.OptimizedCreateHiveTableAsSelectCommand
-
- model(Vector) - 接口 中的方法org.apache.spark.ml.ann.Topology
-
- model(long) - 接口 中的方法org.apache.spark.ml.ann.Topology
-
- model() - 类 中的方法org.apache.spark.ml.FitEnd
-
- Model<M extends Model<M>> - org.apache.spark.ml中的类
-
- Model() - 类 的构造器org.apache.spark.ml.Model
-
- models() - 类 中的方法org.apache.spark.ml.classification.OneVsRestModel
-
- modelType() - 类 中的方法org.apache.spark.ml.classification.NaiveBayes
-
- modelType() - 类 中的方法org.apache.spark.ml.classification.NaiveBayesModel
-
- modelType() - 接口 中的方法org.apache.spark.ml.classification.NaiveBayesParams
-
The model type which is a string (case-sensitive).
- modelType() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel
-
- modelType() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV2_0$.Data
-
- MODIFY_ACLS() - 类 中的静态方法org.apache.spark.internal.config.UI
-
- MODIFY_ACLS_GROUPS() - 类 中的静态方法org.apache.spark.internal.config.UI
-
- MODULE$ - 类 中的静态变量org.apache.spark.graphx.PartitionStrategy.CanonicalRandomVertexCut$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.graphx.PartitionStrategy.EdgePartition1D$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.graphx.PartitionStrategy.EdgePartition2D$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.graphx.PartitionStrategy.RandomVertexCut$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.internal.io.FileCommitProtocol.EmptyTaskCommitMessage$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.InternalAccumulator.input$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.InternalAccumulator.output$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.InternalAccumulator.shuffleRead$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.InternalAccumulator.shuffleWrite$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.evaluation.SquaredEuclideanSilhouette.ClusterStats$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.feature.Word2VecModel.Word2VecModelWriter$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.Pipeline.SharedReadWrite$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.recommendation.ALS.InBlock$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.recommendation.ALS.Rating$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.recommendation.ALS.RatingBlock$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.regression.GeneralizedLinearRegression.Binomial$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.regression.GeneralizedLinearRegression.CLogLog$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.regression.GeneralizedLinearRegression.Family$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.regression.GeneralizedLinearRegression.FamilyAndLink$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.regression.GeneralizedLinearRegression.Gamma$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.regression.GeneralizedLinearRegression.Gaussian$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.regression.GeneralizedLinearRegression.Identity$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.regression.GeneralizedLinearRegression.Inverse$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.regression.GeneralizedLinearRegression.Link$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.regression.GeneralizedLinearRegression.Log$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.regression.GeneralizedLinearRegression.Logit$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.regression.GeneralizedLinearRegression.Poisson$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.regression.GeneralizedLinearRegression.Probit$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.regression.GeneralizedLinearRegression.Sqrt$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.regression.GeneralizedLinearRegression.Tweedie$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.tree.DecisionTreeModelReadWrite.NodeData$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.tree.DecisionTreeModelReadWrite.SplitData$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ml.tree.EnsembleModelReadWrite.EnsembleNodeData$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.classification.impl.GLMClassificationModel.SaveLoadV1_0$.Data$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.classification.impl.GLMClassificationModel.SaveLoadV1_0$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV1_0$.Data$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV1_0$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV2_0$.Data$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV2_0$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.clustering.BisectingKMeansModel.SaveLoadV1_0$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.clustering.BisectingKMeansModel.SaveLoadV2_0$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.clustering.BisectingKMeansModel.SaveLoadV3_0$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.clustering.KMeansModel.SaveLoadV1_0$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.clustering.KMeansModel.SaveLoadV2_0$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.clustering.PowerIterationClustering.Assignment$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.clustering.PowerIterationClusteringModel.SaveLoadV1_0$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.feature.ChiSqSelectorModel.SaveLoadV1_0$.Data$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.feature.ChiSqSelectorModel.SaveLoadV1_0$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.fpm.FPGrowthModel.SaveLoadV1_0$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.fpm.PrefixSpan.Postfix$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.fpm.PrefixSpan.Prefix$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.fpm.PrefixSpanModel.SaveLoadV1_0$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.recommendation.MatrixFactorizationModel.SaveLoadV1_0$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.regression.impl.GLMRegressionModel.SaveLoadV1_0$.Data$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.regression.impl.GLMRegressionModel.SaveLoadV1_0$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.stat.test.ChiSqTest.Method$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.stat.test.ChiSqTest.NullHypothesis$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.stat.test.KolmogorovSmirnovTest.NullHypothesis$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.NodeData$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.PredictData$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.SplitData$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.rdd.HadoopRDD.HadoopMapPartitionsWithSplitRDD$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.rdd.NewHadoopRDD.NewHadoopMapPartitionsWithSplitRDD$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.AddWebUIFilter$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.GetExecutorLossReason$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.KillExecutors$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.KillExecutorsOnHost$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.KillTask$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.LaunchTask$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterClusterManager$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisteredExecutor$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterExecutor$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterExecutorFailed$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RemoveExecutor$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RemoveWorker$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RequestExecutors$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RetrieveDelegationTokens$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RetrieveLastAllocatedExecutorId$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RetrieveSparkAppConfig$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.ReviveOffers$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.SetupDriver$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.Shutdown$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.SparkAppConfig$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.StatusUpdate$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.StopDriver$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.StopExecutor$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.StopExecutors$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.UpdateDelegationTokens$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.serializer.SerializationDebugger.ObjectStreamClassMethods$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.sql.connector.catalog.LookupCatalog.AsTableIdentifier$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.sql.connector.catalog.LookupCatalog.AsTemporaryViewIdentifier$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.sql.connector.catalog.LookupCatalog.CatalogAndIdentifierParts$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.sql.connector.catalog.LookupCatalog.CatalogAndNamespace$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.sql.connector.catalog.LookupCatalog.CatalogObjectIdentifier$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.sql.hive.HiveShim.HiveFunctionWrapper$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.sql.hive.HiveStrategies.HiveTableScans$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.sql.hive.HiveStrategies.Scripts$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.sql.RelationalGroupedDataset.CubeType$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.sql.RelationalGroupedDataset.GroupByType$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.sql.RelationalGroupedDataset.PivotType$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.sql.RelationalGroupedDataset.RollupType$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.sql.types.Decimal.DecimalAsIfIntegral$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.sql.types.Decimal.DecimalIsFractional$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.sql.types.DecimalType.Expression$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.sql.types.DecimalType.Fixed$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.BlockLocationsAndStatus$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.BlockManagerHeartbeat$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.GetBlockStatus$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.GetExecutorEndpointRef$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.GetLocations$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.GetLocationsAndStatus$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.GetLocationsMultipleBlockIds$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.GetMatchingBlockIds$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.GetMemoryStatus$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.GetPeers$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.GetStorageStatus$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.IsExecutorAlive$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.RegisterBlockManager$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.RemoveBlock$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.RemoveBroadcast$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.RemoveExecutor$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.RemoveRdd$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.RemoveShuffle$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.ReplicateBlock$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.StopBlockManagerMaster$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.TriggerThreadDump$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.storage.BlockManagerMessages.UpdateBlockInfo$
-
Static reference to the singleton instance of this Scala object.
- MODULE$ - 类 中的静态变量org.apache.spark.ui.JettyUtils.ServletParams$
-
Static reference to the singleton instance of this Scala object.
- monotonically_increasing_id() - 类 中的静态方法org.apache.spark.sql.functions
-
A column expression that generates monotonically increasing 64-bit integers.
- month(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Extracts the month as an integer from a given date/timestamp/string.
- months(String) - 类 中的静态方法org.apache.spark.sql.connector.expressions.Expressions
-
Create a monthly transform for a timestamp or date column.
- months(String) - 类 中的静态方法org.apache.spark.sql.connector.expressions.LogicalExpressions
-
- months(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
A transform for timestamps and dates to partition data into months.
- months_between(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns number of months between dates start and end.
- months_between(Column, Column, boolean) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns number of months between dates end and start.
- msDurationToString(long) - 类 中的静态方法org.apache.spark.util.Utils
-
Returns a human-readable string representing a duration such as "35ms"
- MsSqlServerDialect - org.apache.spark.sql.jdbc中的类
-
- MsSqlServerDialect() - 类 的构造器org.apache.spark.sql.jdbc.MsSqlServerDialect
-
- mu() - 类 中的方法org.apache.spark.mllib.stat.distribution.MultivariateGaussian
-
- MulticlassClassificationEvaluator - org.apache.spark.ml.evaluation中的类
-
Evaluator for multiclass classification, which expects input columns: prediction, label,
weight (optional) and probability (only for logLoss).
- MulticlassClassificationEvaluator(String) - 类 的构造器org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- MulticlassClassificationEvaluator() - 类 的构造器org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- MulticlassMetrics - org.apache.spark.mllib.evaluation中的类
-
Evaluator for multiclass classification.
- MulticlassMetrics(RDD<? extends Product>) - 类 的构造器org.apache.spark.mllib.evaluation.MulticlassMetrics
-
- MultilabelClassificationEvaluator - org.apache.spark.ml.evaluation中的类
-
:: Experimental ::
Evaluator for multi-label classification, which expects two input
columns: prediction and label.
- MultilabelClassificationEvaluator(String) - 类 的构造器org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator
-
- MultilabelClassificationEvaluator() - 类 的构造器org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator
-
- MultilabelMetrics - org.apache.spark.mllib.evaluation中的类
-
Evaluator for multilabel classification.
- MultilabelMetrics(RDD<Tuple2<double[], double[]>>) - 类 的构造器org.apache.spark.mllib.evaluation.MultilabelMetrics
-
- multiLabelValidator(int) - 类 中的静态方法org.apache.spark.mllib.util.DataValidators
-
Function to check if labels used for k class multi-label classification are
in the range of {0, 1, ..., k - 1}.
- MultilayerPerceptronClassificationModel - org.apache.spark.ml.classification中的类
-
Classification model based on the Multilayer Perceptron.
- MultilayerPerceptronClassifier - org.apache.spark.ml.classification中的类
-
Classifier trainer based on the Multilayer Perceptron.
- MultilayerPerceptronClassifier(String) - 类 的构造器org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
- MultilayerPerceptronClassifier() - 类 的构造器org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
- MultilayerPerceptronParams - org.apache.spark.ml.classification中的接口
-
Params for Multilayer Perceptron.
- MultipartIdentifierHelper(Seq<String>) - 类 的构造器org.apache.spark.sql.connector.catalog.CatalogV2Implicits.MultipartIdentifierHelper
-
- multiply(DenseMatrix) - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Convenience method for Matrix-DenseMatrix multiplication.
- multiply(DenseVector) - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Convenience method for Matrix-DenseVector multiplication.
- multiply(Vector) - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Convenience method for Matrix-Vector multiplication.
- multiply(BlockMatrix) - 类 中的方法org.apache.spark.mllib.linalg.distributed.BlockMatrix
-
- multiply(BlockMatrix, int) - 类 中的方法org.apache.spark.mllib.linalg.distributed.BlockMatrix
-
- multiply(Matrix) - 类 中的方法org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
-
Multiply this matrix by a local matrix on the right.
- multiply(Matrix) - 类 中的方法org.apache.spark.mllib.linalg.distributed.RowMatrix
-
Multiply this matrix by a local matrix on the right.
- multiply(DenseMatrix) - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
Convenience method for Matrix-DenseMatrix multiplication.
- multiply(DenseVector) - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
Convenience method for Matrix-DenseVector multiplication.
- multiply(Vector) - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
Convenience method for Matrix-Vector multiplication.
- multiply(Object) - 类 中的方法org.apache.spark.sql.Column
-
Multiplication of this expression and another expression.
- MultivariateGaussian - org.apache.spark.ml.stat.distribution中的类
-
This class provides basic functionality for a Multivariate Gaussian (Normal) Distribution.
- MultivariateGaussian(Vector, Matrix) - 类 的构造器org.apache.spark.ml.stat.distribution.MultivariateGaussian
-
- MultivariateGaussian - org.apache.spark.mllib.stat.distribution中的类
-
:: DeveloperApi ::
This class provides basic functionality for a Multivariate Gaussian (Normal) Distribution.
- MultivariateGaussian(Vector, Matrix) - 类 的构造器org.apache.spark.mllib.stat.distribution.MultivariateGaussian
-
- MultivariateOnlineSummarizer - org.apache.spark.mllib.stat中的类
-
:: DeveloperApi ::
MultivariateOnlineSummarizer implements
MultivariateStatisticalSummary to compute the mean,
variance, minimum, maximum, counts, and nonzero counts for instances in sparse or dense vector
format in an online fashion.
- MultivariateOnlineSummarizer() - 类 的构造器org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
-
- MultivariateStatisticalSummary - org.apache.spark.mllib.stat中的接口
-
Trait for multivariate statistical summary of a data matrix.
- MutableAggregationBuffer - org.apache.spark.sql.expressions中的类
-
A Row representing a mutable aggregation buffer.
- MutableAggregationBuffer() - 类 的构造器org.apache.spark.sql.expressions.MutableAggregationBuffer
-
- MutablePair<T1,T2> - org.apache.spark.util中的类
-
:: DeveloperApi ::
A tuple of 2 elements.
- MutablePair(T1, T2) - 类 的构造器org.apache.spark.util.MutablePair
-
- MutablePair() - 类 的构造器org.apache.spark.util.MutablePair
-
No-arg constructor for serialization
- MutableURLClassLoader - org.apache.spark.util中的类
-
URL class loader that exposes the `addURL` method in URLClassLoader.
- MutableURLClassLoader(URL[], ClassLoader) - 类 的构造器org.apache.spark.util.MutableURLClassLoader
-
- myName() - 类 中的方法org.apache.spark.util.InnerClosureFinder
-
- MySQLDialect - org.apache.spark.sql.jdbc中的类
-
- MySQLDialect() - 类 的构造器org.apache.spark.sql.jdbc.MySQLDialect
-
- p() - 类 中的方法org.apache.spark.ml.feature.Normalizer
-
Normalization in L^p^ space.
- PagedTable<T> - org.apache.spark.ui中的接口
-
A paged table that will generate a HTML table for a specified page and also the page navigation.
- pageLink(int) - 接口 中的方法org.apache.spark.ui.PagedTable
-
Return a link to jump to a page.
- pageNavigation(int, int, int) - 接口 中的方法org.apache.spark.ui.PagedTable
-
Return a page navigation.
- pageNumberFormField() - 接口 中的方法org.apache.spark.ui.PagedTable
-
- pageRank(double, double) - 类 中的方法org.apache.spark.graphx.GraphOps
-
Run a dynamic version of PageRank returning a graph with vertex attributes containing the
PageRank and edge attributes containing the normalized edge weight.
- PageRank - org.apache.spark.graphx.lib中的类
-
PageRank algorithm implementation.
- PageRank() - 类 的构造器org.apache.spark.graphx.lib.PageRank
-
- pageSizeFormField() - 接口 中的方法org.apache.spark.ui.PagedTable
-
- PairDStreamFunctions<K,V> - org.apache.spark.streaming.dstream中的类
-
Extra functions available on DStream of (key, value) pairs through an implicit conversion.
- PairDStreamFunctions(DStream<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>, Ordering<K>) - 类 的构造器org.apache.spark.streaming.dstream.PairDStreamFunctions
-
- PairFlatMapFunction<T,K,V> - org.apache.spark.api.java.function中的接口
-
A function that returns zero or more key-value pair records from each input record.
- PairFunction<T,K,V> - org.apache.spark.api.java.function中的接口
-
A function that returns key-value pairs (Tuple2<K, V>), and can be used to
construct PairRDDs.
- PairRDDFunctions<K,V> - org.apache.spark.rdd中的类
-
Extra functions available on RDDs of (key, value) pairs through an implicit conversion.
- PairRDDFunctions(RDD<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>, Ordering<K>) - 类 的构造器org.apache.spark.rdd.PairRDDFunctions
-
- PairwiseRRDD<T> - org.apache.spark.api.r中的类
-
Form an RDD[(Int, Array[Byte])] from key-value pairs returned from R.
- PairwiseRRDD(RDD<T>, int, byte[], String, byte[], Object[], ClassTag<T>) - 类 的构造器org.apache.spark.api.r.PairwiseRRDD
-
- parallelism() - 类 中的方法org.apache.spark.ml.classification.OneVsRest
-
- parallelism() - 接口 中的方法org.apache.spark.ml.param.shared.HasParallelism
-
The number of threads to use when running parallel algorithms.
- parallelism() - 类 中的方法org.apache.spark.ml.tuning.CrossValidator
-
- parallelism() - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplit
-
- parallelize(List<T>, int) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Distribute a local Scala collection to form an RDD.
- parallelize(List<T>) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Distribute a local Scala collection to form an RDD.
- parallelize(Seq<T>, int, ClassTag<T>) - 类 中的方法org.apache.spark.SparkContext
-
Distribute a local Scala collection to form an RDD.
- parallelizeDoubles(List<Double>, int) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Distribute a local Scala collection to form an RDD.
- parallelizeDoubles(List<Double>) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Distribute a local Scala collection to form an RDD.
- parallelizePairs(List<Tuple2<K, V>>, int) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Distribute a local Scala collection to form an RDD.
- parallelizePairs(List<Tuple2<K, V>>) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Distribute a local Scala collection to form an RDD.
- Param<T> - org.apache.spark.ml.param中的类
-
:: DeveloperApi ::
A param with self-contained documentation and optionally default value.
- Param(String, String, String, Function1<T, Object>) - 类 的构造器org.apache.spark.ml.param.Param
-
- Param(Identifiable, String, String, Function1<T, Object>) - 类 的构造器org.apache.spark.ml.param.Param
-
- Param(String, String, String) - 类 的构造器org.apache.spark.ml.param.Param
-
- Param(Identifiable, String, String) - 类 的构造器org.apache.spark.ml.param.Param
-
- param() - 类 中的方法org.apache.spark.ml.param.ParamPair
-
- ParamGridBuilder - org.apache.spark.ml.tuning中的类
-
Builder for a param grid used in grid search-based model selection.
- ParamGridBuilder() - 类 的构造器org.apache.spark.ml.tuning.ParamGridBuilder
-
- ParamMap - org.apache.spark.ml.param中的类
-
A param to value map.
- ParamMap() - 类 的构造器org.apache.spark.ml.param.ParamMap
-
Creates an empty param map.
- paramMap() - 接口 中的方法org.apache.spark.ml.param.Params
-
Internal param map for user-supplied values.
- ParamPair<T> - org.apache.spark.ml.param中的类
-
A param and its value.
- ParamPair(Param<T>, T) - 类 的构造器org.apache.spark.ml.param.ParamPair
-
- params() - 类 中的方法org.apache.spark.ml.clustering.PowerIterationClustering
-
- params() - 类 中的方法org.apache.spark.ml.evaluation.Evaluator
-
- params() - 类 中的方法org.apache.spark.ml.fpm.PrefixSpan
-
- params() - 类 中的方法org.apache.spark.ml.param.JavaParams
-
- Params - org.apache.spark.ml.param中的接口
-
:: DeveloperApi ::
Trait for components that take parameters.
- params() - 接口 中的方法org.apache.spark.ml.param.Params
-
Returns all params sorted by their names.
- params() - 类 中的方法org.apache.spark.ml.PipelineStage
-
- ParamValidators - org.apache.spark.ml.param中的类
-
:: DeveloperApi ::
Factory methods for common validation functions for Param.isValid.
- ParamValidators() - 类 的构造器org.apache.spark.ml.param.ParamValidators
-
- parent() - 类 中的方法org.apache.spark.ml.Model
-
The parent estimator that produced this model.
- parent() - 类 中的方法org.apache.spark.ml.param.Param
-
- parent() - 接口 中的方法org.apache.spark.scheduler.Schedulable
-
- ParentClassLoader - org.apache.spark.util中的类
-
A class loader which makes some protected methods in ClassLoader accessible.
- ParentClassLoader(ClassLoader) - 类 的构造器org.apache.spark.util.ParentClassLoader
-
- parentIds() - 类 中的方法org.apache.spark.scheduler.StageInfo
-
- parentIds() - 类 中的方法org.apache.spark.storage.RDDInfo
-
- parentIndex(int) - 类 中的静态方法org.apache.spark.mllib.tree.model.Node
-
Get the parent index of the given node, or 0 if it is the root.
- parmap(Seq<I>, String, int, Function1<I, O>) - 类 中的静态方法org.apache.spark.util.ThreadUtils
-
Transforms input collection by applying the given function to each element in parallel fashion.
- parquet(String...) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads a Parquet file, returning the result as a DataFrame.
- parquet(String) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads a Parquet file, returning the result as a DataFrame.
- parquet(Seq<String>) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads a Parquet file, returning the result as a DataFrame.
- parquet(String) - 类 中的方法org.apache.spark.sql.DataFrameWriter
-
Saves the content of the DataFrame in Parquet format at the specified path.
- parquet(String) - 类 中的方法org.apache.spark.sql.streaming.DataStreamReader
-
Loads a Parquet file stream, returning the result as a DataFrame.
- parse(String) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- parse(String) - 类 中的静态方法org.apache.spark.mllib.linalg.Vectors
-
Parses a string resulted from
Vector.toString into a
Vector.
- parse(String) - 类 中的静态方法org.apache.spark.mllib.regression.LabeledPoint
-
Parses a string resulted from
LabeledPoint#toString into
an
LabeledPoint.
- parse(String) - 类 中的静态方法org.apache.spark.mllib.util.NumericParser
-
Parses a string into a Double, an Array[Double], or a Seq[Any].
- parseAll(Parsers.Parser<T>, Reader<Object>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- parseAll(Parsers.Parser<T>, Reader) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- parseAll(Parsers.Parser<T>, CharSequence) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- parseAllocatedFromJsonFile(String) - 类 中的静态方法org.apache.spark.resource.ResourceUtils
-
- parseAllResourceRequests(SparkConf, String) - 类 中的静态方法org.apache.spark.resource.ResourceUtils
-
- parseHostPort(String) - 类 中的静态方法org.apache.spark.util.Utils
-
- parseIgnoreCase(Class<E>, String) - 类 中的静态方法org.apache.spark.util.EnumUtil
-
- parseJson(String) - 类 中的静态方法org.apache.spark.resource.ResourceInformation
-
- parseJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.resource.ResourceInformation
-
- Parser(Function1<Reader<Object>, Parsers.ParseResult<T>>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- parseResourceRequest(SparkConf, ResourceID) - 类 中的静态方法org.apache.spark.resource.ResourceUtils
-
- parseResourceRequirements(SparkConf, String) - 类 中的静态方法org.apache.spark.resource.ResourceUtils
-
- parseStandaloneMasterUrls(String) - 类 中的静态方法org.apache.spark.util.Utils
-
Split the comma delimited string of master URLs into a list.
- PartialResult<R> - org.apache.spark.partial中的类
-
- PartialResult(R, boolean) - 类 的构造器org.apache.spark.partial.PartialResult
-
- Partition - org.apache.spark中的接口
-
An identifier for a partition in an RDD.
- partition() - 类 中的方法org.apache.spark.scheduler.AskPermissionToCommitOutput
-
- partition() - 类 中的方法org.apache.spark.sql.hive.execution.InsertIntoHiveTable
-
- partition(String) - 类 中的方法org.apache.spark.status.LiveRDD
-
- partitionBy(Partitioner) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return a copy of the RDD partitioned using the specified partitioner.
- partitionBy(PartitionStrategy) - 类 中的方法org.apache.spark.graphx.Graph
-
Repartitions the edges in the graph according to partitionStrategy.
- partitionBy(PartitionStrategy, int) - 类 中的方法org.apache.spark.graphx.Graph
-
Repartitions the edges in the graph according to partitionStrategy.
- partitionBy(PartitionStrategy) - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- partitionBy(PartitionStrategy, int) - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- partitionBy(Partitioner) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Return a copy of the RDD partitioned using the specified partitioner.
- partitionBy(String...) - 类 中的方法org.apache.spark.sql.DataFrameWriter
-
Partitions the output by the given columns on the file system.
- partitionBy(Seq<String>) - 类 中的方法org.apache.spark.sql.DataFrameWriter
-
Partitions the output by the given columns on the file system.
- partitionBy(String, String...) - 类 中的静态方法org.apache.spark.sql.expressions.Window
-
Creates a
WindowSpec with the partitioning defined.
- partitionBy(Column...) - 类 中的静态方法org.apache.spark.sql.expressions.Window
-
Creates a
WindowSpec with the partitioning defined.
- partitionBy(String, Seq<String>) - 类 中的静态方法org.apache.spark.sql.expressions.Window
-
Creates a
WindowSpec with the partitioning defined.
- partitionBy(Seq<Column>) - 类 中的静态方法org.apache.spark.sql.expressions.Window
-
Creates a
WindowSpec with the partitioning defined.
- partitionBy(String, String...) - 类 中的方法org.apache.spark.sql.expressions.WindowSpec
-
- partitionBy(Column...) - 类 中的方法org.apache.spark.sql.expressions.WindowSpec
-
- partitionBy(String, Seq<String>) - 类 中的方法org.apache.spark.sql.expressions.WindowSpec
-
- partitionBy(Seq<Column>) - 类 中的方法org.apache.spark.sql.expressions.WindowSpec
-
- partitionBy(String...) - 类 中的方法org.apache.spark.sql.streaming.DataStreamWriter
-
Partitions the output by the given columns on the file system.
- partitionBy(Seq<String>) - 类 中的方法org.apache.spark.sql.streaming.DataStreamWriter
-
Partitions the output by the given columns on the file system.
- PartitionCoalescer - org.apache.spark.rdd中的接口
-
::DeveloperApi::
A PartitionCoalescer defines how to coalesce the partitions of a given RDD.
- partitionedBy(Column, Seq<Column>) - 接口 中的方法org.apache.spark.sql.CreateTableWriter
-
Partition the output table created by create, createOrReplace, or replace using
the given columns or transforms.
- partitionedBy(Column, Column...) - 类 中的方法org.apache.spark.sql.DataFrameWriterV2
-
- partitionedBy(Column, Seq<Column>) - 类 中的方法org.apache.spark.sql.DataFrameWriterV2
-
- partitioner() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
The partitioner of this RDD.
- partitioner() - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
If partitionsRDD already has a partitioner, use it.
- partitioner() - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- Partitioner - org.apache.spark中的类
-
An object that defines how the elements in a key-value pair RDD are partitioned by key.
- Partitioner() - 类 的构造器org.apache.spark.Partitioner
-
- partitioner() - 类 中的方法org.apache.spark.rdd.CoGroupedRDD
-
- partitioner() - 类 中的方法org.apache.spark.rdd.RDD
-
Optionally overridden by subclasses to specify how they are partitioned.
- partitioner() - 类 中的方法org.apache.spark.rdd.ShuffledRDD
-
- partitioner() - 类 中的方法org.apache.spark.ShuffleDependency
-
- partitioner(Partitioner) - 类 中的方法org.apache.spark.streaming.StateSpec
-
Set the partitioner by which the state RDDs generated by mapWithState will be partitioned.
- PartitionGroup - org.apache.spark.rdd中的类
-
::DeveloperApi::
A group of Partitions
param: prefLoc preferred location for the partition group
- PartitionGroup(Option<String>) - 类 的构造器org.apache.spark.rdd.PartitionGroup
-
- partitionGroupOrdering() - 类 中的方法org.apache.spark.rdd.DefaultPartitionCoalescer
-
Accessor for nested Scala object
- partitionGroupOrdering$() - 类 的构造器org.apache.spark.rdd.DefaultPartitionCoalescer.partitionGroupOrdering$
-
- partitionId() - 类 中的方法org.apache.spark.BarrierTaskContext
-
- partitionID() - 类 中的方法org.apache.spark.TaskCommitDenied
-
- partitionId() - 类 中的方法org.apache.spark.TaskContext
-
The ID of the RDD partition that is computed by this task.
- partitioning() - 接口 中的方法org.apache.spark.sql.connector.catalog.Table
-
Returns the physical partitioning of this table.
- Partitioning - org.apache.spark.sql.connector.read.partitioning中的接口
-
- PartitionOffset - org.apache.spark.sql.connector.read.streaming中的接口
-
Used for per-partition offsets in continuous processing.
- PartitionPruning - org.apache.spark.sql.dynamicpruning中的类
-
Dynamic partition pruning optimization is performed based on the type and
selectivity of the join operation.
- PartitionPruning() - 类 的构造器org.apache.spark.sql.dynamicpruning.PartitionPruning
-
- PartitionPruningRDD<T> - org.apache.spark.rdd中的类
-
:: DeveloperApi ::
An RDD used to prune RDD partitions/partitions so we can avoid launching tasks on
all partitions.
- PartitionPruningRDD(RDD<T>, Function1<Object, Object>, ClassTag<T>) - 类 的构造器org.apache.spark.rdd.PartitionPruningRDD
-
- PartitionReader<T> - org.apache.spark.sql.connector.read中的接口
-
- PartitionReaderFactory - org.apache.spark.sql.connector.read中的接口
-
- partitions() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Set of partitions in this RDD.
- partitions() - 类 中的方法org.apache.spark.rdd.PartitionGroup
-
- partitions() - 类 中的方法org.apache.spark.rdd.RDD
-
Get the array of partitions of this RDD, taking into account whether the
RDD is checkpointed or not.
- partitions() - 类 中的方法org.apache.spark.status.api.v1.RDDStorageInfo
-
- partitionsRDD() - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
- partitionsRDD() - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- PartitionStrategy - org.apache.spark.graphx中的接口
-
Represents the way edges are assigned to edge partitions based on their source and destination
vertex IDs.
- PartitionStrategy.CanonicalRandomVertexCut$ - org.apache.spark.graphx中的类
-
Assigns edges to partitions by hashing the source and destination vertex IDs in a canonical
direction, resulting in a random vertex cut that colocates all edges between two vertices,
regardless of direction.
- PartitionStrategy.EdgePartition1D$ - org.apache.spark.graphx中的类
-
Assigns edges to partitions using only the source vertex ID, colocating edges with the same
source.
- PartitionStrategy.EdgePartition2D$ - org.apache.spark.graphx中的类
-
Assigns edges to partitions using a 2D partitioning of the sparse edge adjacency matrix,
guaranteeing a 2 * sqrt(numParts) bound on vertex replication.
- PartitionStrategy.RandomVertexCut$ - org.apache.spark.graphx中的类
-
Assigns edges to partitions by hashing the source and destination vertex IDs, resulting in a
random vertex cut that colocates all same-direction edges between two vertices.
- PartitionTypeHelper(StructType) - 类 的构造器org.apache.spark.sql.connector.catalog.CatalogV2Implicits.PartitionTypeHelper
-
- path() - 类 中的方法org.apache.spark.ml.LoadInstanceStart
-
- path() - 类 中的方法org.apache.spark.ml.SaveInstanceEnd
-
- path() - 类 中的方法org.apache.spark.ml.SaveInstanceStart
-
- path() - 类 中的方法org.apache.spark.scheduler.InputFormatInfo
-
- path() - 类 中的方法org.apache.spark.scheduler.SplitInfo
-
- pattern() - 类 中的方法org.apache.spark.ml.feature.RegexTokenizer
-
Regex pattern used to match delimiters if gaps is true or tokens if gaps is false.
- pc() - 类 中的方法org.apache.spark.ml.feature.PCAModel
-
- pc() - 类 中的方法org.apache.spark.mllib.feature.PCAModel
-
- PCA - org.apache.spark.ml.feature中的类
-
PCA trains a model to project vectors to a lower dimensional space of the top PCA!.
- PCA(String) - 类 的构造器org.apache.spark.ml.feature.PCA
-
- PCA() - 类 的构造器org.apache.spark.ml.feature.PCA
-
- PCA - org.apache.spark.mllib.feature中的类
-
A feature transformer that projects vectors to a low-dimensional space using PCA.
- PCA(int) - 类 的构造器org.apache.spark.mllib.feature.PCA
-
- PCAModel - org.apache.spark.ml.feature中的类
-
- PCAModel - org.apache.spark.mllib.feature中的类
-
Model fitted by
PCA that can project vectors to a low-dimensional space using PCA.
- PCAParams - org.apache.spark.ml.feature中的接口
-
- PCAUtil - org.apache.spark.mllib.feature中的类
-
- PCAUtil() - 类 的构造器org.apache.spark.mllib.feature.PCAUtil
-
- pdf(Vector) - 类 中的方法org.apache.spark.ml.stat.distribution.MultivariateGaussian
-
Returns density of this multivariate Gaussian at given point, x
- pdf(Vector) - 类 中的方法org.apache.spark.mllib.stat.distribution.MultivariateGaussian
-
Returns density of this multivariate Gaussian at given point, x
- PEAK_EXECUTION_MEMORY() - 类 中的静态方法org.apache.spark.InternalAccumulator
-
- PEAK_EXECUTION_MEMORY() - 类 中的静态方法org.apache.spark.ui.jobs.TaskDetailsClassNames
-
- PEAK_EXECUTION_MEMORY() - 类 中的静态方法org.apache.spark.ui.ToolTips
-
- PEAK_MEM() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- peakExecutionMemory() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- peakExecutionMemory() - 类 中的方法org.apache.spark.status.api.v1.TaskMetricDistributions
-
- peakExecutionMemory() - 类 中的方法org.apache.spark.status.api.v1.TaskMetrics
-
- peakExecutorMetrics() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- peakMemoryMetrics() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- PEARSON() - 类 中的静态方法org.apache.spark.mllib.stat.test.ChiSqTest
-
- PearsonCorrelation - org.apache.spark.mllib.stat.correlation中的类
-
Compute Pearson correlation for two RDDs of the type RDD[Double] or the correlation matrix
for an RDD of the type RDD[Vector].
- PearsonCorrelation() - 类 的构造器org.apache.spark.mllib.stat.correlation.PearsonCorrelation
-
- percent_rank() - 类 中的静态方法org.apache.spark.sql.functions
-
Window function: returns the relative rank (i.e. percentile) of rows within a window partition.
- percentile() - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- percentile() - 类 中的方法org.apache.spark.ml.feature.ChiSqSelectorModel
-
- percentile() - 接口 中的方法org.apache.spark.ml.feature.ChiSqSelectorParams
-
Percentile of features that selector will select, ordered by statistics value descending.
- percentile() - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelector
-
- percentiles() - 类 中的静态方法org.apache.spark.scheduler.StatsReportListener
-
- percentilesHeader() - 类 中的静态方法org.apache.spark.scheduler.StatsReportListener
-
- persist(StorageLevel) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Set this RDD's storage level to persist its values across operations after the first time
it is computed.
- persist(StorageLevel) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Set this RDD's storage level to persist its values across operations after the first time
it is computed.
- persist(StorageLevel) - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Set this RDD's storage level to persist its values across operations after the first time
it is computed.
- persist(StorageLevel) - 类 中的方法org.apache.spark.graphx.Graph
-
Caches the vertices and edges associated with this graph at the specified storage level,
ignoring any target storage levels previously set.
- persist(StorageLevel) - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
Persists the edge partitions at the specified storage level, ignoring any existing target
storage level.
- persist(StorageLevel) - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- persist(StorageLevel) - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
Persists the vertex partitions at the specified storage level, ignoring any existing target
storage level.
- persist(StorageLevel) - 类 中的方法org.apache.spark.mllib.linalg.distributed.BlockMatrix
-
Persists the underlying RDD with the specified storage level.
- persist(StorageLevel) - 类 中的方法org.apache.spark.rdd.HadoopRDD
-
- persist(StorageLevel) - 类 中的方法org.apache.spark.rdd.NewHadoopRDD
-
- persist(StorageLevel) - 类 中的方法org.apache.spark.rdd.RDD
-
Set this RDD's storage level to persist its values across operations after the first time
it is computed.
- persist() - 类 中的方法org.apache.spark.rdd.RDD
-
Persist this RDD with the default storage level (MEMORY_ONLY).
- persist() - 类 中的方法org.apache.spark.sql.Dataset
-
Persist this Dataset with the default storage level (MEMORY_AND_DISK).
- persist(StorageLevel) - 类 中的方法org.apache.spark.sql.Dataset
-
Persist this Dataset with the given storage level.
- persist() - 类 中的方法org.apache.spark.streaming.api.java.JavaDStream
-
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
- persist(StorageLevel) - 类 中的方法org.apache.spark.streaming.api.java.JavaDStream
-
Persist the RDDs of this DStream with the given storage level
- persist() - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
- persist(StorageLevel) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Persist the RDDs of this DStream with the given storage level
- persist(StorageLevel) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Persist the RDDs of this DStream with the given storage level
- persist() - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Persist RDDs of this DStream with the default storage level (MEMORY_ONLY_SER)
- personalizedPageRank(long, double, double) - 类 中的方法org.apache.spark.graphx.GraphOps
-
Run personalized PageRank for a given vertex, such that all random walks
are started relative to the source node.
- phrase(Parsers.Parser<T>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- pi() - 类 中的方法org.apache.spark.ml.classification.NaiveBayesModel
-
- pi() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel
-
- pi() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV1_0$.Data
-
- pi() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV2_0$.Data
-
- pickBin(Partition, RDD<?>, double, org.apache.spark.rdd.DefaultPartitionCoalescer.PartitionLocations) - 类 中的方法org.apache.spark.rdd.DefaultPartitionCoalescer
-
Takes a parent RDD partition and decides which of the partition groups to put it in
Takes locality into account, but also uses power of 2 choices to load balance
It strikes a balance between the two using the balanceSlack variable
- pickRandomVertex() - 类 中的方法org.apache.spark.graphx.GraphOps
-
Picks a random vertex from the graph and returns its ID.
- pipe(String) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return an RDD created by piping elements to a forked external process.
- pipe(List<String>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return an RDD created by piping elements to a forked external process.
- pipe(List<String>, Map<String, String>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return an RDD created by piping elements to a forked external process.
- pipe(List<String>, Map<String, String>, boolean, int) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return an RDD created by piping elements to a forked external process.
- pipe(List<String>, Map<String, String>, boolean, int, String) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return an RDD created by piping elements to a forked external process.
- pipe(String) - 类 中的方法org.apache.spark.rdd.RDD
-
Return an RDD created by piping elements to a forked external process.
- pipe(String, Map<String, String>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return an RDD created by piping elements to a forked external process.
- pipe(Seq<String>, Map<String, String>, Function1<Function1<String, BoxedUnit>, BoxedUnit>, Function2<T, Function1<String, BoxedUnit>, BoxedUnit>, boolean, int, String) - 类 中的方法org.apache.spark.rdd.RDD
-
Return an RDD created by piping elements to a forked external process.
- Pipeline - org.apache.spark.ml中的类
-
A simple pipeline, which acts as an estimator.
- Pipeline(String) - 类 的构造器org.apache.spark.ml.Pipeline
-
- Pipeline() - 类 的构造器org.apache.spark.ml.Pipeline
-
- Pipeline.SharedReadWrite$ - org.apache.spark.ml中的类
-
- PipelineModel - org.apache.spark.ml中的类
-
Represents a fitted pipeline.
- PipelineStage - org.apache.spark.ml中的类
-
- PipelineStage() - 类 的构造器org.apache.spark.ml.PipelineStage
-
- pivot(String) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Pivots a column of the current DataFrame and performs the specified aggregation.
- pivot(String, Seq<Object>) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Pivots a column of the current DataFrame and performs the specified aggregation.
- pivot(String, List<Object>) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
(Java-specific) Pivots a column of the current DataFrame and performs the specified
aggregation.
- pivot(Column) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Pivots a column of the current DataFrame and performs the specified aggregation.
- pivot(Column, Seq<Object>) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Pivots a column of the current DataFrame and performs the specified aggregation.
- pivot(Column, List<Object>) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
(Java-specific) Pivots a column of the current DataFrame and performs the specified
aggregation.
- PivotType$() - 类 的构造器org.apache.spark.sql.RelationalGroupedDataset.PivotType$
-
- plan() - 异常错误 中的方法org.apache.spark.sql.AnalysisException
-
- PlanDynamicPruningFilters - org.apache.spark.sql.dynamicpruning中的类
-
This planner rule aims at rewriting dynamic pruning predicates in order to reuse the
results of broadcast.
- PlanDynamicPruningFilters(SparkSession) - 类 的构造器org.apache.spark.sql.dynamicpruning.PlanDynamicPruningFilters
-
- planInputPartitions() - 接口 中的方法org.apache.spark.sql.connector.read.Batch
-
- planInputPartitions(Offset) - 接口 中的方法org.apache.spark.sql.connector.read.streaming.ContinuousStream
-
- planInputPartitions(Offset, Offset) - 接口 中的方法org.apache.spark.sql.connector.read.streaming.MicroBatchStream
-
- plus(Object) - 类 中的方法org.apache.spark.sql.Column
-
Sum of this expression and another expression.
- plus(byte, byte) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- plus(Decimal, Decimal) - 接口 中的方法org.apache.spark.sql.types.Decimal.DecimalIsConflicted
-
- plus(Decimal, Decimal) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- plus(double, double) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- plus(float, float) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- plus(int, int) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- plus(long, long) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- plus(short, short) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- plus(Duration) - 类 中的方法org.apache.spark.streaming.Duration
-
- plus(Duration) - 类 中的方法org.apache.spark.streaming.Time
-
- pmml() - 接口 中的方法org.apache.spark.mllib.pmml.export.PMMLModelExport
-
Holder of the exported model in PMML format
- PMMLExportable - org.apache.spark.mllib.pmml中的接口
-
:: DeveloperApi ::
Export model to the PMML format
Predictive Model Markup Language (PMML) is an XML-based file format
developed by the Data Mining Group (www.dmg.org).
- PMMLKMeansModelWriter - org.apache.spark.ml.clustering中的类
-
A writer for KMeans that handles the "pmml" format
- PMMLKMeansModelWriter() - 类 的构造器org.apache.spark.ml.clustering.PMMLKMeansModelWriter
-
- PMMLLinearRegressionModelWriter - org.apache.spark.ml.regression中的类
-
A writer for LinearRegression that handles the "pmml" format
- PMMLLinearRegressionModelWriter() - 类 的构造器org.apache.spark.ml.regression.PMMLLinearRegressionModelWriter
-
- PMMLModelExport - org.apache.spark.mllib.pmml.export中的接口
-
- PMMLModelExportFactory - org.apache.spark.mllib.pmml.export中的类
-
- PMMLModelExportFactory() - 类 的构造器org.apache.spark.mllib.pmml.export.PMMLModelExportFactory
-
- pmod(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the positive value of dividend mod divisor.
- point() - 类 中的方法org.apache.spark.mllib.feature.VocabWord
-
- POINTS() - 类 中的静态方法org.apache.spark.mllib.clustering.StreamingKMeans
-
- pointSilhouetteCoefficient(Set<Object>, double, long, Function1<Object, Object>) - 类 中的静态方法org.apache.spark.ml.evaluation.CosineSilhouette
-
- pointSilhouetteCoefficient(Set<Object>, double, long, Function1<Object, Object>) - 类 中的静态方法org.apache.spark.ml.evaluation.SquaredEuclideanSilhouette
-
- POISON_PILL() - 类 中的静态方法org.apache.spark.scheduler.AsyncEventQueue
-
- PoisonPill() - 类 中的静态方法org.apache.spark.rpc.netty.MessageLoop
-
A poison inbox that indicates the message loop should stop processing messages.
- Poisson$() - 类 的构造器org.apache.spark.ml.regression.GeneralizedLinearRegression.Poisson$
-
- PoissonBounds - org.apache.spark.util.random中的类
-
Utility functions that help us determine bounds on adjusted sampling rate to guarantee exact
sample sizes with high confidence when sampling with replacement.
- PoissonBounds() - 类 的构造器org.apache.spark.util.random.PoissonBounds
-
- PoissonGenerator - org.apache.spark.mllib.random中的类
-
:: DeveloperApi ::
Generates i.i.d. samples from the Poisson distribution with the given mean.
- PoissonGenerator(double) - 类 的构造器org.apache.spark.mllib.random.PoissonGenerator
-
- poissonJavaRDD(JavaSparkContext, double, long, int, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
Java-friendly version of RandomRDDs.poissonRDD.
- poissonJavaRDD(JavaSparkContext, double, long, int) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
RandomRDDs.poissonJavaRDD with the default seed.
- poissonJavaRDD(JavaSparkContext, double, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
RandomRDDs.poissonJavaRDD with the default number of partitions and the default seed.
- poissonJavaVectorRDD(JavaSparkContext, double, long, int, int, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
Java-friendly version of RandomRDDs.poissonVectorRDD.
- poissonJavaVectorRDD(JavaSparkContext, double, long, int, int) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
RandomRDDs.poissonJavaVectorRDD with the default seed.
- poissonJavaVectorRDD(JavaSparkContext, double, long, int) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
RandomRDDs.poissonJavaVectorRDD with the default number of partitions and the default seed.
- poissonRDD(SparkContext, double, long, int, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
Generates an RDD comprised of i.i.d.
- PoissonSampler<T> - org.apache.spark.util.random中的类
-
:: DeveloperApi ::
A sampler for sampling with replacement, based on values drawn from Poisson distribution.
- PoissonSampler(double, boolean) - 类 的构造器org.apache.spark.util.random.PoissonSampler
-
- PoissonSampler(double) - 类 的构造器org.apache.spark.util.random.PoissonSampler
-
- poissonVectorRDD(SparkContext, double, long, int, int, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
Generates an RDD[Vector] with vectors containing i.i.d.
- PolynomialExpansion - org.apache.spark.ml.feature中的类
-
Perform feature expansion in a polynomial space.
- PolynomialExpansion(String) - 类 的构造器org.apache.spark.ml.feature.PolynomialExpansion
-
- PolynomialExpansion() - 类 的构造器org.apache.spark.ml.feature.PolynomialExpansion
-
- pool() - 类 中的方法org.apache.spark.serializer.KryoSerializer
-
- popStdev() - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Compute the population standard deviation of this RDD's elements.
- popStdev() - 类 中的方法org.apache.spark.rdd.DoubleRDDFunctions
-
Compute the population standard deviation of this RDD's elements.
- popStdev() - 类 中的方法org.apache.spark.util.StatCounter
-
Return the population standard deviation of the values.
- popVariance() - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Compute the population variance of this RDD's elements.
- popVariance() - 类 中的方法org.apache.spark.rdd.DoubleRDDFunctions
-
Compute the population variance of this RDD's elements.
- popVariance() - 类 中的方法org.apache.spark.util.StatCounter
-
Return the population variance of the values.
- port() - 接口 中的方法org.apache.spark.SparkExecutorInfo
-
- port() - 类 中的方法org.apache.spark.SparkExecutorInfoImpl
-
- port() - 类 中的方法org.apache.spark.storage.BlockManagerId
-
- PortableDataStream - org.apache.spark.input中的类
-
A class that allows DataStreams to be serialized and moved around by not creating them
until they need to be read
- PortableDataStream(CombineFileSplit, TaskAttemptContext, Integer) - 类 的构造器org.apache.spark.input.PortableDataStream
-
- portMaxRetries(SparkConf) - 类 中的静态方法org.apache.spark.util.Utils
-
Maximum number of retries when binding to a port before giving up.
- posexplode(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a new row for each element with position in the given array or map column.
- posexplode_outer(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a new row for each element with position in the given array or map column.
- position() - 类 中的方法org.apache.spark.storage.ReadableChannelFileRegion
-
- positioned(Function0<Parsers.Parser<T>>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- post(String, InboxMessage) - 类 中的方法org.apache.spark.rpc.netty.DedicatedMessageLoop
-
- post(String, InboxMessage) - 类 中的方法org.apache.spark.rpc.netty.MessageLoop
-
- post(String, InboxMessage) - 类 中的方法org.apache.spark.rpc.netty.SharedMessageLoop
-
- post(SparkListenerEvent) - 类 中的方法org.apache.spark.scheduler.AsyncEventQueue
-
- Postfix$() - 类 的构造器org.apache.spark.mllib.fpm.PrefixSpan.Postfix$
-
- PostgresDialect - org.apache.spark.sql.jdbc中的类
-
- PostgresDialect() - 类 的构造器org.apache.spark.sql.jdbc.PostgresDialect
-
- postStartHook() - 接口 中的方法org.apache.spark.scheduler.TaskScheduler
-
- postToAll(E) - 接口 中的方法org.apache.spark.util.ListenerBus
-
Post the event to all registered listeners.
- pow(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the value of the first argument raised to the power of the second argument.
- pow(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the value of the first argument raised to the power of the second argument.
- pow(String, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the value of the first argument raised to the power of the second argument.
- pow(String, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the value of the first argument raised to the power of the second argument.
- pow(Column, double) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the value of the first argument raised to the power of the second argument.
- pow(String, double) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the value of the first argument raised to the power of the second argument.
- pow(double, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the value of the first argument raised to the power of the second argument.
- pow(double, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the value of the first argument raised to the power of the second argument.
- PowerIterationClustering - org.apache.spark.ml.clustering中的类
-
Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by
Lin and Cohen.
- PowerIterationClustering() - 类 的构造器org.apache.spark.ml.clustering.PowerIterationClustering
-
- PowerIterationClustering - org.apache.spark.mllib.clustering中的类
-
Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by
Lin and Cohen.
- PowerIterationClustering() - 类 的构造器org.apache.spark.mllib.clustering.PowerIterationClustering
-
Constructs a PIC instance with default parameters: {k: 2, maxIterations: 100,
initMode: "random"}.
- PowerIterationClustering.Assignment - org.apache.spark.mllib.clustering中的类
-
Cluster assignment.
- PowerIterationClustering.Assignment$ - org.apache.spark.mllib.clustering中的类
-
- PowerIterationClusteringModel - org.apache.spark.mllib.clustering中的类
-
- PowerIterationClusteringModel(int, RDD<PowerIterationClustering.Assignment>) - 类 的构造器org.apache.spark.mllib.clustering.PowerIterationClusteringModel
-
- PowerIterationClusteringModel.SaveLoadV1_0$ - org.apache.spark.mllib.clustering中的类
-
- PowerIterationClusteringParams - org.apache.spark.ml.clustering中的接口
-
Common params for PowerIterationClustering
- PowerIterationClusteringWrapper - org.apache.spark.ml.r中的类
-
- PowerIterationClusteringWrapper() - 类 的构造器org.apache.spark.ml.r.PowerIterationClusteringWrapper
-
- pr() - 接口 中的方法org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
-
Returns the precision-recall curve, which is a Dataframe containing
two fields recall, precision with (0.0, 1.0) prepended to it.
- pr() - 类 中的方法org.apache.spark.ml.classification.BinaryLogisticRegressionSummaryImpl
-
- pr() - 类 中的方法org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
-
Returns the precision-recall curve, which is an RDD of (recall, precision),
NOT (precision, recall), with (0.0, p) prepended to it, where p is the precision
associated with the lowest recall on the curve.
- preciseSize() - 接口 中的方法org.apache.spark.storage.memory.MemoryEntryBuilder
-
- Precision - org.apache.spark.mllib.evaluation.binary中的类
-
Precision.
- Precision() - 类 的构造器org.apache.spark.mllib.evaluation.binary.Precision
-
- precision(double) - 类 中的方法org.apache.spark.mllib.evaluation.MulticlassMetrics
-
Returns precision for a given label (category)
- precision() - 类 中的方法org.apache.spark.mllib.evaluation.MultilabelMetrics
-
Returns document-based precision averaged by the number of documents
- precision(double) - 类 中的方法org.apache.spark.mllib.evaluation.MultilabelMetrics
-
Returns precision for a given label (category)
- precision() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- precision() - 类 中的方法org.apache.spark.sql.types.DecimalType
-
- precisionAt(int) - 类 中的方法org.apache.spark.mllib.evaluation.RankingMetrics
-
Compute the average precision of all the queries, truncated at ranking position k.
- precisionByLabel() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionSummary
-
Returns precision for each label (category).
- precisionByThreshold() - 接口 中的方法org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
-
Returns a dataframe with two fields (threshold, precision) curve.
- precisionByThreshold() - 类 中的方法org.apache.spark.ml.classification.BinaryLogisticRegressionSummaryImpl
-
- precisionByThreshold() - 类 中的方法org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
-
Returns the (threshold, precision) curve.
- predict(Vector) - 接口 中的方法org.apache.spark.ml.ann.TopologyModel
-
Prediction of the model.
- predict(FeaturesType) - 类 中的方法org.apache.spark.ml.classification.ClassificationModel
-
Predict label for the given features.
- predict(Vector) - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- predict(Vector) - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- predict(Vector) - 类 中的方法org.apache.spark.ml.classification.LinearSVCModel
-
- predict(Vector) - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
Predict label for the given feature vector.
- predict(Vector) - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel
-
Predict label for the given features.
- predict(Vector) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
- predict(Vector) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- predict(Vector) - 类 中的方法org.apache.spark.ml.clustering.KMeansModel
-
- predict(FeaturesType) - 类 中的方法org.apache.spark.ml.PredictionModel
-
Predict label for the given features.
- predict(Vector) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- predict(Vector) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- predict(Vector) - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- predict(Vector) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
- predict(Vector) - 类 中的方法org.apache.spark.ml.regression.LinearRegressionModel
-
- predict(Vector) - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- predict(RDD<Vector>) - 接口 中的方法org.apache.spark.mllib.classification.ClassificationModel
-
Predict values for the given data set using the model trained.
- predict(Vector) - 接口 中的方法org.apache.spark.mllib.classification.ClassificationModel
-
Predict values for a single data point using the model trained.
- predict(JavaRDD<Vector>) - 接口 中的方法org.apache.spark.mllib.classification.ClassificationModel
-
Predict values for examples stored in a JavaRDD.
- predict(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel
-
- predict(Vector) - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel
-
- predict(Vector) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeansModel
-
Predicts the index of the cluster that the input point belongs to.
- predict(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeansModel
-
Predicts the indices of the clusters that the input points belong to.
- predict(JavaRDD<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeansModel
-
Java-friendly version of predict().
- predict(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixtureModel
-
Maps given points to their cluster indices.
- predict(Vector) - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixtureModel
-
Maps given point to its cluster index.
- predict(JavaRDD<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixtureModel
-
Java-friendly version of predict()
- predict(Vector) - 类 中的方法org.apache.spark.mllib.clustering.KMeansModel
-
Returns the cluster index that a given point belongs to.
- predict(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.KMeansModel
-
Maps given points to their cluster indices.
- predict(JavaRDD<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.KMeansModel
-
Maps given points to their cluster indices.
- predict(int, int) - 类 中的方法org.apache.spark.mllib.recommendation.MatrixFactorizationModel
-
Predict the rating of one user for one product.
- predict(RDD<Tuple2<Object, Object>>) - 类 中的方法org.apache.spark.mllib.recommendation.MatrixFactorizationModel
-
Predict the rating of many users for many products.
- predict(JavaPairRDD<Integer, Integer>) - 类 中的方法org.apache.spark.mllib.recommendation.MatrixFactorizationModel
-
Java-friendly version of MatrixFactorizationModel.predict.
- predict(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.regression.GeneralizedLinearModel
-
Predict values for the given data set using the model trained.
- predict(Vector) - 类 中的方法org.apache.spark.mllib.regression.GeneralizedLinearModel
-
Predict values for a single data point using the model trained.
- predict(RDD<Object>) - 类 中的方法org.apache.spark.mllib.regression.IsotonicRegressionModel
-
Predict labels for provided features.
- predict(JavaDoubleRDD) - 类 中的方法org.apache.spark.mllib.regression.IsotonicRegressionModel
-
Predict labels for provided features.
- predict(double) - 类 中的方法org.apache.spark.mllib.regression.IsotonicRegressionModel
-
Predict a single label.
- predict(RDD<Vector>) - 接口 中的方法org.apache.spark.mllib.regression.RegressionModel
-
Predict values for the given data set using the model trained.
- predict(Vector) - 接口 中的方法org.apache.spark.mllib.regression.RegressionModel
-
Predict values for a single data point using the model trained.
- predict(JavaRDD<Vector>) - 接口 中的方法org.apache.spark.mllib.regression.RegressionModel
-
Predict values for examples stored in a JavaRDD.
- predict(Vector) - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel
-
Predict values for a single data point using the model trained.
- predict(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel
-
Predict values for the given data set using the model trained.
- predict(JavaRDD<Vector>) - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel
-
Predict values for the given data set using the model trained.
- predict() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.NodeData
-
- predict() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.PredictData
-
- predict() - 类 中的方法org.apache.spark.mllib.tree.model.Node
-
- predict(Vector) - 类 中的方法org.apache.spark.mllib.tree.model.Node
-
predict value if node is not leaf
- Predict - org.apache.spark.mllib.tree.model中的类
-
:: DeveloperApi ::
Predicted value for a node
param: predict predicted value
param: prob probability of the label (classification only)
- Predict(double, double) - 类 的构造器org.apache.spark.mllib.tree.model.Predict
-
- predict() - 类 中的方法org.apache.spark.mllib.tree.model.Predict
-
- PredictData(double, double) - 类 的构造器org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.PredictData
-
- PredictData$() - 类 的构造器org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.PredictData$
-
- prediction() - 类 中的方法org.apache.spark.ml.tree.DecisionTreeModelReadWrite.NodeData
-
- prediction() - 类 中的方法org.apache.spark.ml.tree.InternalNode
-
- prediction() - 类 中的方法org.apache.spark.ml.tree.LeafNode
-
- prediction() - 类 中的方法org.apache.spark.ml.tree.Node
-
Prediction a leaf node makes, or which an internal node would make if it were a leaf node
- predictionCol() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionSummary
-
Field in "predictions" which gives the prediction of each class.
- predictionCol() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionSummaryImpl
-
- predictionCol() - 类 中的方法org.apache.spark.ml.classification.OneVsRest
-
- predictionCol() - 类 中的方法org.apache.spark.ml.classification.OneVsRestModel
-
- predictionCol() - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeans
-
- predictionCol() - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
- predictionCol() - 类 中的方法org.apache.spark.ml.clustering.ClusteringSummary
-
- predictionCol() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixture
-
- predictionCol() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- predictionCol() - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- predictionCol() - 类 中的方法org.apache.spark.ml.clustering.KMeansModel
-
- predictionCol() - 类 中的方法org.apache.spark.ml.evaluation.ClusteringEvaluator
-
- predictionCol() - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- predictionCol() - 类 中的方法org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator
-
- predictionCol() - 类 中的方法org.apache.spark.ml.evaluation.RankingEvaluator
-
- predictionCol() - 类 中的方法org.apache.spark.ml.evaluation.RegressionEvaluator
-
- predictionCol() - 类 中的方法org.apache.spark.ml.fpm.FPGrowth
-
- predictionCol() - 类 中的方法org.apache.spark.ml.fpm.FPGrowthModel
-
- predictionCol() - 接口 中的方法org.apache.spark.ml.param.shared.HasPredictionCol
-
Param for prediction column name.
- predictionCol() - 类 中的方法org.apache.spark.ml.PredictionModel
-
- predictionCol() - 类 中的方法org.apache.spark.ml.Predictor
-
- predictionCol() - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- predictionCol() - 类 中的方法org.apache.spark.ml.recommendation.ALSModel
-
- predictionCol() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- predictionCol() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- predictionCol() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary
-
Field in "predictions" which gives the predicted value of each instance.
- predictionCol() - 类 中的方法org.apache.spark.ml.regression.IsotonicRegression
-
- predictionCol() - 类 中的方法org.apache.spark.ml.regression.IsotonicRegressionModel
-
- predictionCol() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionSummary
-
- PredictionModel<FeaturesType,M extends PredictionModel<FeaturesType,M>> - org.apache.spark.ml中的类
-
:: DeveloperApi ::
Abstraction for a model for prediction tasks (regression and classification).
- PredictionModel() - 类 的构造器org.apache.spark.ml.PredictionModel
-
- predictions() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionSummary
-
Dataframe output by the model's transform method.
- predictions() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionSummaryImpl
-
- predictions() - 类 中的方法org.apache.spark.ml.clustering.ClusteringSummary
-
- predictions() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary
-
Predictions output by the model's transform method.
- predictions() - 类 中的方法org.apache.spark.ml.regression.IsotonicRegressionModel
-
Predictions associated with the boundaries at the same index, monotone because of isotonic
regression.
- predictions() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionSummary
-
- predictions() - 类 中的方法org.apache.spark.mllib.regression.IsotonicRegressionModel
-
- predictLeaf(Vector) - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeModel
-
- predictLeaf(Vector) - 接口 中的方法org.apache.spark.ml.tree.TreeEnsembleModel
-
- predictOn(DStream<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.StreamingKMeans
-
Use the clustering model to make predictions on batches of data from a DStream.
- predictOn(JavaDStream<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.StreamingKMeans
-
Java-friendly version of predictOn.
- predictOn(DStream<Vector>) - 类 中的方法org.apache.spark.mllib.regression.StreamingLinearAlgorithm
-
Use the model to make predictions on batches of data from a DStream
- predictOn(JavaDStream<Vector>) - 类 中的方法org.apache.spark.mllib.regression.StreamingLinearAlgorithm
-
Java-friendly version of predictOn.
- predictOnValues(DStream<Tuple2<K, Vector>>, ClassTag<K>) - 类 中的方法org.apache.spark.mllib.clustering.StreamingKMeans
-
Use the model to make predictions on the values of a DStream and carry over its keys.
- predictOnValues(JavaPairDStream<K, Vector>) - 类 中的方法org.apache.spark.mllib.clustering.StreamingKMeans
-
Java-friendly version of predictOnValues.
- predictOnValues(DStream<Tuple2<K, Vector>>, ClassTag<K>) - 类 中的方法org.apache.spark.mllib.regression.StreamingLinearAlgorithm
-
Use the model to make predictions on the values of a DStream and carry over its keys.
- predictOnValues(JavaPairDStream<K, Vector>) - 类 中的方法org.apache.spark.mllib.regression.StreamingLinearAlgorithm
-
Java-friendly version of predictOnValues.
- Predictor<FeaturesType,Learner extends Predictor<FeaturesType,Learner,M>,M extends PredictionModel<FeaturesType,M>> - org.apache.spark.ml中的类
-
:: DeveloperApi ::
Abstraction for prediction problems (regression and classification).
- Predictor() - 类 的构造器org.apache.spark.ml.Predictor
-
- PredictorParams - org.apache.spark.ml中的接口
-
(private[ml]) Trait for parameters for prediction (regression and classification).
- predictProbabilities(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel
-
Predict values for the given data set using the model trained.
- predictProbabilities(Vector) - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel
-
Predict posterior class probabilities for a single data point using the model trained.
- predictProbability(Vector) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- predictQuantiles(Vector) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- predictRaw(Vector) - 接口 中的方法org.apache.spark.ml.ann.TopologyModel
-
Raw prediction of the model.
- predictSoft(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixtureModel
-
Given the input vectors, return the membership value of each vector
to all mixture components.
- predictSoft(Vector) - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixtureModel
-
Given the input vector, return the membership values to all mixture components.
- PREFER_CONFIGURED_MASTER_ADDRESS() - 类 中的静态方法org.apache.spark.internal.config.Worker
-
- preferredLocation() - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Override this to specify a preferred location (hostname).
- preferredLocations(Partition) - 类 中的方法org.apache.spark.rdd.RDD
-
Get the preferred locations of a partition, taking into account whether the
RDD is checkpointed.
- preferredLocations() - 接口 中的方法org.apache.spark.sql.connector.read.InputPartition
-
The preferred locations where the input partition reader returned by this partition can run
faster, but Spark does not guarantee to run the input partition reader on these locations.
- Prefix$() - 类 的构造器org.apache.spark.mllib.fpm.PrefixSpan.Prefix$
-
- prefixesToRewrite() - 类 中的方法org.apache.spark.ml.feature.VectorAttributeRewriter
-
- PrefixSpan - org.apache.spark.ml.fpm中的类
-
A parallel PrefixSpan algorithm to mine frequent sequential patterns.
- PrefixSpan(String) - 类 的构造器org.apache.spark.ml.fpm.PrefixSpan
-
- PrefixSpan() - 类 的构造器org.apache.spark.ml.fpm.PrefixSpan
-
- PrefixSpan - org.apache.spark.mllib.fpm中的类
-
A parallel PrefixSpan algorithm to mine frequent sequential patterns.
- PrefixSpan() - 类 的构造器org.apache.spark.mllib.fpm.PrefixSpan
-
Constructs a default instance with default parameters
{minSupport: 0.1, maxPatternLength: 10, maxLocalProjDBSize: 32000000L}.
- PrefixSpan.FreqSequence<Item> - org.apache.spark.mllib.fpm中的类
-
Represents a frequent sequence.
- PrefixSpan.Postfix$ - org.apache.spark.mllib.fpm中的类
-
- PrefixSpan.Prefix$ - org.apache.spark.mllib.fpm中的类
-
- PrefixSpanModel<Item> - org.apache.spark.mllib.fpm中的类
-
Model fitted by
PrefixSpan
param: freqSequences frequent sequences
- PrefixSpanModel(RDD<PrefixSpan.FreqSequence<Item>>) - 类 的构造器org.apache.spark.mllib.fpm.PrefixSpanModel
-
- PrefixSpanModel.SaveLoadV1_0$ - org.apache.spark.mllib.fpm中的类
-
- PrefixSpanWrapper - org.apache.spark.ml.r中的类
-
- PrefixSpanWrapper() - 类 的构造器org.apache.spark.ml.r.PrefixSpanWrapper
-
- prefLoc() - 类 中的方法org.apache.spark.rdd.PartitionGroup
-
- pregel(A, int, EdgeDirection, Function3<Object, VD, A, VD>, Function1<EdgeTriplet<VD, ED>, Iterator<Tuple2<Object, A>>>, Function2<A, A, A>, ClassTag<A>) - 类 中的方法org.apache.spark.graphx.GraphOps
-
Execute a Pregel-like iterative vertex-parallel abstraction.
- Pregel - org.apache.spark.graphx中的类
-
Implements a Pregel-like bulk-synchronous message-passing API.
- Pregel() - 类 的构造器org.apache.spark.graphx.Pregel
-
- prepareWritable(Writable, Seq<Tuple2<String, String>>) - 类 中的静态方法org.apache.spark.sql.hive.HiveShim
-
- prepareWrite(SparkSession, Job, Map<String, String>, StructType) - 类 中的方法org.apache.spark.sql.hive.execution.HiveFileFormat
-
- prepareWrite(SparkSession, Job, Map<String, String>, StructType) - 类 中的方法org.apache.spark.sql.hive.orc.OrcFileFormat
-
- prependBaseUri(HttpServletRequest, String, String) - 类 中的静态方法org.apache.spark.ui.UIUtils
-
- prettyJson() - 接口 中的方法org.apache.spark.sql.Row
-
The pretty (i.e. indented) JSON representation of this row.
- prettyJson() - 类 中的方法org.apache.spark.sql.streaming.SinkProgress
-
The pretty (i.e. indented) JSON representation of this progress.
- prettyJson() - 类 中的方法org.apache.spark.sql.streaming.SourceProgress
-
The pretty (i.e. indented) JSON representation of this progress.
- prettyJson() - 类 中的方法org.apache.spark.sql.streaming.StateOperatorProgress
-
The pretty (i.e. indented) JSON representation of this progress.
- prettyJson() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryProgress
-
The pretty (i.e. indented) JSON representation of this progress.
- prettyJson() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryStatus
-
The pretty (i.e. indented) JSON representation of this status.
- prettyJson() - 类 中的静态方法org.apache.spark.sql.types.BinaryType
-
- prettyJson() - 类 中的静态方法org.apache.spark.sql.types.BooleanType
-
- prettyJson() - 类 中的静态方法org.apache.spark.sql.types.ByteType
-
- prettyJson() - 类 中的静态方法org.apache.spark.sql.types.CalendarIntervalType
-
- prettyJson() - 类 中的方法org.apache.spark.sql.types.DataType
-
The pretty (i.e. indented) JSON representation of this data type.
- prettyJson() - 类 中的静态方法org.apache.spark.sql.types.DateType
-
- prettyJson() - 类 中的静态方法org.apache.spark.sql.types.DoubleType
-
- prettyJson() - 类 中的静态方法org.apache.spark.sql.types.FloatType
-
- prettyJson() - 类 中的静态方法org.apache.spark.sql.types.IntegerType
-
- prettyJson() - 类 中的静态方法org.apache.spark.sql.types.LongType
-
- prettyJson() - 类 中的静态方法org.apache.spark.sql.types.NullType
-
- prettyJson() - 类 中的静态方法org.apache.spark.sql.types.ShortType
-
- prettyJson() - 类 中的静态方法org.apache.spark.sql.types.StringType
-
- prettyJson() - 类 中的静态方法org.apache.spark.sql.types.TimestampType
-
- prettyPrint() - 类 中的方法org.apache.spark.streaming.Duration
-
- prev() - 类 中的方法org.apache.spark.rdd.ShuffledRDD
-
- prev() - 类 中的方法org.apache.spark.status.LiveRDDPartition
-
- print() - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Print the first ten elements of each RDD generated in this DStream.
- print(int) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Print the first num elements of each RDD generated in this DStream.
- print() - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Print the first ten elements of each RDD generated in this DStream.
- print(int) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Print the first num elements of each RDD generated in this DStream.
- printErrorAndExit(String) - 接口 中的方法org.apache.spark.util.CommandLineLoggingUtils
-
- printMessage(String) - 接口 中的方法org.apache.spark.util.CommandLineLoggingUtils
-
- printSchema() - 类 中的方法org.apache.spark.sql.Dataset
-
Prints the schema to the console in a nice tree format.
- printSchema(int) - 类 中的方法org.apache.spark.sql.Dataset
-
Prints the schema up to the given level to the console in a nice tree format.
- printStats() - 类 中的方法org.apache.spark.streaming.scheduler.StatsReportListener
-
- printStream() - 接口 中的方法org.apache.spark.util.CommandLineLoggingUtils
-
- printTreeString() - 类 中的方法org.apache.spark.sql.types.StructType
-
- prioritize(BlockManagerId, Seq<BlockManagerId>, HashSet<BlockManagerId>, BlockId, int) - 类 中的方法org.apache.spark.storage.BasicBlockReplicationPolicy
-
Method to prioritize a bunch of candidate peers of a block manager.
- prioritize(BlockManagerId, Seq<BlockManagerId>, HashSet<BlockManagerId>, BlockId, int) - 接口 中的方法org.apache.spark.storage.BlockReplicationPolicy
-
Method to prioritize a bunch of candidate peers of a block
- prioritize(BlockManagerId, Seq<BlockManagerId>, HashSet<BlockManagerId>, BlockId, int) - 类 中的方法org.apache.spark.storage.RandomBlockReplicationPolicy
-
Method to prioritize a bunch of candidate peers of a block.
- priority() - 接口 中的方法org.apache.spark.scheduler.Schedulable
-
- prob() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.PredictData
-
- prob() - 类 中的方法org.apache.spark.mllib.tree.model.Predict
-
- ProbabilisticClassificationModel<FeaturesType,M extends ProbabilisticClassificationModel<FeaturesType,M>> - org.apache.spark.ml.classification中的类
-
- ProbabilisticClassificationModel() - 类 的构造器org.apache.spark.ml.classification.ProbabilisticClassificationModel
-
- ProbabilisticClassifier<FeaturesType,E extends ProbabilisticClassifier<FeaturesType,E,M>,M extends ProbabilisticClassificationModel<FeaturesType,M>> - org.apache.spark.ml.classification中的类
-
:: DeveloperApi ::
Single-label binary or multiclass classifier which can output class conditional probabilities.
- ProbabilisticClassifier() - 类 的构造器org.apache.spark.ml.classification.ProbabilisticClassifier
-
- ProbabilisticClassifierParams - org.apache.spark.ml.classification中的接口
-
(private[classification]) Params for probabilistic classification.
- probabilities() - 类 中的静态方法org.apache.spark.scheduler.StatsReportListener
-
- probability() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureSummary
-
- probabilityCol() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionSummary
-
Field in "predictions" which gives the probability of each class as a vector.
- probabilityCol() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionSummaryImpl
-
- probabilityCol() - 类 中的方法org.apache.spark.ml.classification.ProbabilisticClassificationModel
-
- probabilityCol() - 类 中的方法org.apache.spark.ml.classification.ProbabilisticClassifier
-
- probabilityCol() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixture
-
- probabilityCol() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- probabilityCol() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureSummary
-
- probabilityCol() - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- probabilityCol() - 接口 中的方法org.apache.spark.ml.param.shared.HasProbabilityCol
-
Param for Column name for predicted class conditional probabilities.
- Probit$() - 类 的构造器org.apache.spark.ml.regression.GeneralizedLinearRegression.Probit$
-
- process(T) - 类 中的方法org.apache.spark.sql.ForeachWriter
-
Called to process the data in the executor side.
- PROCESS_LOCAL() - 类 中的静态方法org.apache.spark.scheduler.TaskLocality
-
- processAllAvailable() - 接口 中的方法org.apache.spark.sql.streaming.StreamingQuery
-
Blocks until all available data in the source has been processed and committed to the sink.
- processedRowsPerSecond() - 类 中的方法org.apache.spark.sql.streaming.SourceProgress
-
- processedRowsPerSecond() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryProgress
-
The aggregate (across all sources) rate at which Spark is processing data.
- processingDelay() - 类 中的方法org.apache.spark.streaming.scheduler.BatchInfo
-
Time taken for the all jobs of this batch to finish processing from the time they started
processing.
- processingEndTime() - 类 中的方法org.apache.spark.streaming.scheduler.BatchInfo
-
- processingStartTime() - 类 中的方法org.apache.spark.streaming.scheduler.BatchInfo
-
- ProcessingTime(long) - 类 中的静态方法org.apache.spark.sql.streaming.Trigger
-
A trigger policy that runs a query periodically based on an interval in processing time.
- ProcessingTime(long, TimeUnit) - 类 中的静态方法org.apache.spark.sql.streaming.Trigger
-
(Java-friendly)
A trigger policy that runs a query periodically based on an interval in processing time.
- ProcessingTime(Duration) - 类 中的静态方法org.apache.spark.sql.streaming.Trigger
-
(Scala-friendly)
A trigger policy that runs a query periodically based on an interval in processing time.
- ProcessingTime(String) - 类 中的静态方法org.apache.spark.sql.streaming.Trigger
-
A trigger policy that runs a query periodically based on an interval in processing time.
- processingTime() - 类 中的方法org.apache.spark.status.api.v1.streaming.BatchInfo
-
- ProcessingTimeTimeout() - 类 中的静态方法org.apache.spark.sql.streaming.GroupStateTimeout
-
Timeout based on processing time.
- processStreamByLine(String, InputStream, Function1<String, BoxedUnit>) - 类 中的静态方法org.apache.spark.util.Utils
-
Return and start a daemon thread that processes the content of the input stream line by line.
- ProcessTreeMetrics - org.apache.spark.metrics中的类
-
- ProcessTreeMetrics() - 类 的构造器org.apache.spark.metrics.ProcessTreeMetrics
-
- producedAttributes() - 类 中的方法org.apache.spark.sql.hive.execution.ScriptTransformationExec
-
- product() - 类 中的方法org.apache.spark.mllib.recommendation.Rating
-
- product(TypeTags.TypeTag<T>) - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for Scala's product type (tuples, case classes, etc).
- productArity() - 类 中的静态方法org.apache.spark.ExpireDeadHosts
-
- productArity() - 类 中的静态方法org.apache.spark.metrics.DirectPoolMemory
-
- productArity() - 类 中的静态方法org.apache.spark.metrics.GarbageCollectionMetrics
-
- productArity() - 类 中的静态方法org.apache.spark.metrics.JVMHeapMemory
-
- productArity() - 类 中的静态方法org.apache.spark.metrics.JVMOffHeapMemory
-
- productArity() - 类 中的静态方法org.apache.spark.metrics.MappedPoolMemory
-
- productArity() - 类 中的静态方法org.apache.spark.metrics.OffHeapExecutionMemory
-
- productArity() - 类 中的静态方法org.apache.spark.metrics.OffHeapStorageMemory
-
- productArity() - 类 中的静态方法org.apache.spark.metrics.OffHeapUnifiedMemory
-
- productArity() - 类 中的静态方法org.apache.spark.metrics.OnHeapExecutionMemory
-
- productArity() - 类 中的静态方法org.apache.spark.metrics.OnHeapStorageMemory
-
- productArity() - 类 中的静态方法org.apache.spark.metrics.OnHeapUnifiedMemory
-
- productArity() - 类 中的静态方法org.apache.spark.metrics.ProcessTreeMetrics
-
- productArity() - 类 中的静态方法org.apache.spark.ml.feature.Dot
-
- productArity() - 类 中的静态方法org.apache.spark.ml.feature.EmptyTerm
-
- productArity() - 类 中的静态方法org.apache.spark.Resubmitted
-
- productArity() - 类 中的静态方法org.apache.spark.rpc.netty.OnStart
-
- productArity() - 类 中的静态方法org.apache.spark.rpc.netty.OnStop
-
- productArity() - 类 中的静态方法org.apache.spark.scheduler.AllJobsCancelled
-
- productArity() - 类 中的静态方法org.apache.spark.scheduler.JobSucceeded
-
- productArity() - 类 中的静态方法org.apache.spark.scheduler.ResubmitFailedStages
-
- productArity() - 类 中的静态方法org.apache.spark.scheduler.StopCoordinator
-
- productArity() - 类 中的静态方法org.apache.spark.sql.jdbc.MySQLDialect
-
- productArity() - 类 中的静态方法org.apache.spark.sql.jdbc.OracleDialect
-
- productArity() - 类 中的静态方法org.apache.spark.sql.jdbc.TeradataDialect
-
- productArity() - 类 中的静态方法org.apache.spark.sql.sources.AlwaysFalse
-
- productArity() - 类 中的静态方法org.apache.spark.sql.sources.AlwaysTrue
-
- productArity() - 类 中的静态方法org.apache.spark.sql.types.BinaryType
-
- productArity() - 类 中的静态方法org.apache.spark.sql.types.BooleanType
-
- productArity() - 类 中的静态方法org.apache.spark.sql.types.ByteType
-
- productArity() - 类 中的静态方法org.apache.spark.sql.types.CalendarIntervalType
-
- productArity() - 类 中的静态方法org.apache.spark.sql.types.DateType
-
- productArity() - 类 中的静态方法org.apache.spark.sql.types.DoubleType
-
- productArity() - 类 中的静态方法org.apache.spark.sql.types.FloatType
-
- productArity() - 类 中的静态方法org.apache.spark.sql.types.IntegerType
-
- productArity() - 类 中的静态方法org.apache.spark.sql.types.LongType
-
- productArity() - 类 中的静态方法org.apache.spark.sql.types.NullType
-
- productArity() - 类 中的静态方法org.apache.spark.sql.types.ShortType
-
- productArity() - 类 中的静态方法org.apache.spark.sql.types.StringType
-
- productArity() - 类 中的静态方法org.apache.spark.sql.types.TimestampType
-
- productArity() - 类 中的静态方法org.apache.spark.StopMapOutputTracker
-
- productArity() - 类 中的静态方法org.apache.spark.streaming.kinesis.DefaultCredentials
-
- productArity() - 类 中的静态方法org.apache.spark.streaming.scheduler.AllReceiverIds
-
- productArity() - 类 中的静态方法org.apache.spark.streaming.scheduler.GetAllReceiverInfo
-
- productArity() - 类 中的静态方法org.apache.spark.streaming.scheduler.StopAllReceivers
-
- productArity() - 类 中的静态方法org.apache.spark.Success
-
- productArity() - 类 中的静态方法org.apache.spark.TaskResultLost
-
- productArity() - 类 中的静态方法org.apache.spark.TaskSchedulerIsSet
-
- productArity() - 类 中的静态方法org.apache.spark.UnknownReason
-
- productElement(int) - 类 中的静态方法org.apache.spark.ExpireDeadHosts
-
- productElement(int) - 类 中的静态方法org.apache.spark.metrics.DirectPoolMemory
-
- productElement(int) - 类 中的静态方法org.apache.spark.metrics.GarbageCollectionMetrics
-
- productElement(int) - 类 中的静态方法org.apache.spark.metrics.JVMHeapMemory
-
- productElement(int) - 类 中的静态方法org.apache.spark.metrics.JVMOffHeapMemory
-
- productElement(int) - 类 中的静态方法org.apache.spark.metrics.MappedPoolMemory
-
- productElement(int) - 类 中的静态方法org.apache.spark.metrics.OffHeapExecutionMemory
-
- productElement(int) - 类 中的静态方法org.apache.spark.metrics.OffHeapStorageMemory
-
- productElement(int) - 类 中的静态方法org.apache.spark.metrics.OffHeapUnifiedMemory
-
- productElement(int) - 类 中的静态方法org.apache.spark.metrics.OnHeapExecutionMemory
-
- productElement(int) - 类 中的静态方法org.apache.spark.metrics.OnHeapStorageMemory
-
- productElement(int) - 类 中的静态方法org.apache.spark.metrics.OnHeapUnifiedMemory
-
- productElement(int) - 类 中的静态方法org.apache.spark.metrics.ProcessTreeMetrics
-
- productElement(int) - 类 中的静态方法org.apache.spark.ml.feature.Dot
-
- productElement(int) - 类 中的静态方法org.apache.spark.ml.feature.EmptyTerm
-
- productElement(int) - 类 中的静态方法org.apache.spark.Resubmitted
-
- productElement(int) - 类 中的静态方法org.apache.spark.rpc.netty.OnStart
-
- productElement(int) - 类 中的静态方法org.apache.spark.rpc.netty.OnStop
-
- productElement(int) - 类 中的静态方法org.apache.spark.scheduler.AllJobsCancelled
-
- productElement(int) - 类 中的静态方法org.apache.spark.scheduler.JobSucceeded
-
- productElement(int) - 类 中的静态方法org.apache.spark.scheduler.ResubmitFailedStages
-
- productElement(int) - 类 中的静态方法org.apache.spark.scheduler.StopCoordinator
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.jdbc.MySQLDialect
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.jdbc.OracleDialect
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.jdbc.TeradataDialect
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.sources.AlwaysFalse
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.sources.AlwaysTrue
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.types.BinaryType
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.types.BooleanType
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.types.ByteType
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.types.CalendarIntervalType
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.types.DateType
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.types.DoubleType
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.types.FloatType
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.types.IntegerType
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.types.LongType
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.types.NullType
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.types.ShortType
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.types.StringType
-
- productElement(int) - 类 中的静态方法org.apache.spark.sql.types.TimestampType
-
- productElement(int) - 类 中的静态方法org.apache.spark.StopMapOutputTracker
-
- productElement(int) - 类 中的静态方法org.apache.spark.streaming.kinesis.DefaultCredentials
-
- productElement(int) - 类 中的静态方法org.apache.spark.streaming.scheduler.AllReceiverIds
-
- productElement(int) - 类 中的静态方法org.apache.spark.streaming.scheduler.GetAllReceiverInfo
-
- productElement(int) - 类 中的静态方法org.apache.spark.streaming.scheduler.StopAllReceivers
-
- productElement(int) - 类 中的静态方法org.apache.spark.Success
-
- productElement(int) - 类 中的静态方法org.apache.spark.TaskResultLost
-
- productElement(int) - 类 中的静态方法org.apache.spark.TaskSchedulerIsSet
-
- productElement(int) - 类 中的静态方法org.apache.spark.UnknownReason
-
- productFeatures() - 类 中的方法org.apache.spark.mllib.recommendation.MatrixFactorizationModel
-
- productIterator() - 类 中的静态方法org.apache.spark.ExpireDeadHosts
-
- productIterator() - 类 中的静态方法org.apache.spark.metrics.DirectPoolMemory
-
- productIterator() - 类 中的静态方法org.apache.spark.metrics.GarbageCollectionMetrics
-
- productIterator() - 类 中的静态方法org.apache.spark.metrics.JVMHeapMemory
-
- productIterator() - 类 中的静态方法org.apache.spark.metrics.JVMOffHeapMemory
-
- productIterator() - 类 中的静态方法org.apache.spark.metrics.MappedPoolMemory
-
- productIterator() - 类 中的静态方法org.apache.spark.metrics.OffHeapExecutionMemory
-
- productIterator() - 类 中的静态方法org.apache.spark.metrics.OffHeapStorageMemory
-
- productIterator() - 类 中的静态方法org.apache.spark.metrics.OffHeapUnifiedMemory
-
- productIterator() - 类 中的静态方法org.apache.spark.metrics.OnHeapExecutionMemory
-
- productIterator() - 类 中的静态方法org.apache.spark.metrics.OnHeapStorageMemory
-
- productIterator() - 类 中的静态方法org.apache.spark.metrics.OnHeapUnifiedMemory
-
- productIterator() - 类 中的静态方法org.apache.spark.metrics.ProcessTreeMetrics
-
- productIterator() - 类 中的静态方法org.apache.spark.ml.feature.Dot
-
- productIterator() - 类 中的静态方法org.apache.spark.ml.feature.EmptyTerm
-
- productIterator() - 类 中的静态方法org.apache.spark.Resubmitted
-
- productIterator() - 类 中的静态方法org.apache.spark.rpc.netty.OnStart
-
- productIterator() - 类 中的静态方法org.apache.spark.rpc.netty.OnStop
-
- productIterator() - 类 中的静态方法org.apache.spark.scheduler.AllJobsCancelled
-
- productIterator() - 类 中的静态方法org.apache.spark.scheduler.JobSucceeded
-
- productIterator() - 类 中的静态方法org.apache.spark.scheduler.ResubmitFailedStages
-
- productIterator() - 类 中的静态方法org.apache.spark.scheduler.StopCoordinator
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.jdbc.MySQLDialect
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.jdbc.OracleDialect
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.jdbc.TeradataDialect
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.sources.AlwaysFalse
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.sources.AlwaysTrue
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.types.BinaryType
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.types.BooleanType
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.types.ByteType
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.types.CalendarIntervalType
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.types.DateType
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.types.DoubleType
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.types.FloatType
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.types.IntegerType
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.types.LongType
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.types.NullType
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.types.ShortType
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.types.StringType
-
- productIterator() - 类 中的静态方法org.apache.spark.sql.types.TimestampType
-
- productIterator() - 类 中的静态方法org.apache.spark.StopMapOutputTracker
-
- productIterator() - 类 中的静态方法org.apache.spark.streaming.kinesis.DefaultCredentials
-
- productIterator() - 类 中的静态方法org.apache.spark.streaming.scheduler.AllReceiverIds
-
- productIterator() - 类 中的静态方法org.apache.spark.streaming.scheduler.GetAllReceiverInfo
-
- productIterator() - 类 中的静态方法org.apache.spark.streaming.scheduler.StopAllReceivers
-
- productIterator() - 类 中的静态方法org.apache.spark.Success
-
- productIterator() - 类 中的静态方法org.apache.spark.TaskResultLost
-
- productIterator() - 类 中的静态方法org.apache.spark.TaskSchedulerIsSet
-
- productIterator() - 类 中的静态方法org.apache.spark.UnknownReason
-
- productPrefix() - 类 中的静态方法org.apache.spark.ExpireDeadHosts
-
- productPrefix() - 类 中的静态方法org.apache.spark.metrics.DirectPoolMemory
-
- productPrefix() - 类 中的静态方法org.apache.spark.metrics.GarbageCollectionMetrics
-
- productPrefix() - 类 中的静态方法org.apache.spark.metrics.JVMHeapMemory
-
- productPrefix() - 类 中的静态方法org.apache.spark.metrics.JVMOffHeapMemory
-
- productPrefix() - 类 中的静态方法org.apache.spark.metrics.MappedPoolMemory
-
- productPrefix() - 类 中的静态方法org.apache.spark.metrics.OffHeapExecutionMemory
-
- productPrefix() - 类 中的静态方法org.apache.spark.metrics.OffHeapStorageMemory
-
- productPrefix() - 类 中的静态方法org.apache.spark.metrics.OffHeapUnifiedMemory
-
- productPrefix() - 类 中的静态方法org.apache.spark.metrics.OnHeapExecutionMemory
-
- productPrefix() - 类 中的静态方法org.apache.spark.metrics.OnHeapStorageMemory
-
- productPrefix() - 类 中的静态方法org.apache.spark.metrics.OnHeapUnifiedMemory
-
- productPrefix() - 类 中的静态方法org.apache.spark.metrics.ProcessTreeMetrics
-
- productPrefix() - 类 中的静态方法org.apache.spark.ml.feature.Dot
-
- productPrefix() - 类 中的静态方法org.apache.spark.ml.feature.EmptyTerm
-
- productPrefix() - 类 中的静态方法org.apache.spark.Resubmitted
-
- productPrefix() - 类 中的静态方法org.apache.spark.rpc.netty.OnStart
-
- productPrefix() - 类 中的静态方法org.apache.spark.rpc.netty.OnStop
-
- productPrefix() - 类 中的静态方法org.apache.spark.scheduler.AllJobsCancelled
-
- productPrefix() - 类 中的静态方法org.apache.spark.scheduler.JobSucceeded
-
- productPrefix() - 类 中的静态方法org.apache.spark.scheduler.ResubmitFailedStages
-
- productPrefix() - 类 中的静态方法org.apache.spark.scheduler.StopCoordinator
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.jdbc.MySQLDialect
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.jdbc.OracleDialect
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.jdbc.TeradataDialect
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.sources.AlwaysFalse
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.sources.AlwaysTrue
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.types.BinaryType
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.types.BooleanType
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.types.ByteType
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.types.CalendarIntervalType
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.types.DateType
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.types.DoubleType
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.types.FloatType
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.types.IntegerType
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.types.LongType
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.types.NullType
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.types.ShortType
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.types.StringType
-
- productPrefix() - 类 中的静态方法org.apache.spark.sql.types.TimestampType
-
- productPrefix() - 类 中的静态方法org.apache.spark.StopMapOutputTracker
-
- productPrefix() - 类 中的静态方法org.apache.spark.streaming.kinesis.DefaultCredentials
-
- productPrefix() - 类 中的静态方法org.apache.spark.streaming.scheduler.AllReceiverIds
-
- productPrefix() - 类 中的静态方法org.apache.spark.streaming.scheduler.GetAllReceiverInfo
-
- productPrefix() - 类 中的静态方法org.apache.spark.streaming.scheduler.StopAllReceivers
-
- productPrefix() - 类 中的静态方法org.apache.spark.Success
-
- productPrefix() - 类 中的静态方法org.apache.spark.TaskResultLost
-
- productPrefix() - 类 中的静态方法org.apache.spark.TaskSchedulerIsSet
-
- productPrefix() - 类 中的静态方法org.apache.spark.UnknownReason
-
- progress() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryListener.QueryProgressEvent
-
- project(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Binomial$
-
- project(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression.Gaussian$
-
- properties() - 类 中的方法org.apache.spark.scheduler.SparkListenerJobStart
-
- properties() - 类 中的方法org.apache.spark.scheduler.SparkListenerStageSubmitted
-
- properties() - 接口 中的方法org.apache.spark.sql.connector.catalog.Table
-
Returns the string map of table properties.
- propertiesFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- propertiesToJson(Properties) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- property() - 类 中的方法org.apache.spark.sql.connector.catalog.NamespaceChange.RemoveProperty
-
- property() - 类 中的方法org.apache.spark.sql.connector.catalog.NamespaceChange.SetProperty
-
- property() - 类 中的方法org.apache.spark.sql.connector.catalog.TableChange.RemoveProperty
-
- property() - 类 中的方法org.apache.spark.sql.connector.catalog.TableChange.SetProperty
-
- PROVIDER() - 类 中的静态方法org.apache.spark.internal.config.History
-
- provider() - 类 中的静态方法org.apache.spark.streaming.kinesis.DefaultCredentials
-
- provider() - 接口 中的方法org.apache.spark.streaming.kinesis.SparkAWSCredentials
-
Return an AWSCredentialProvider instance that can be used by the Kinesis Client
Library to authenticate to AWS services (Kinesis, CloudWatch and DynamoDB).
- proxyBase() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.AddWebUIFilter
-
- pruneColumns(StructType) - 接口 中的方法org.apache.spark.sql.connector.read.SupportsPushDownRequiredColumns
-
Applies column pruning w.r.t. the given requiredSchema.
- PrunedFilteredScan - org.apache.spark.sql.sources中的接口
-
A BaseRelation that can eliminate unneeded columns and filter using selected
predicates before producing an RDD containing all matching tuples as Row objects.
- PrunedScan - org.apache.spark.sql.sources中的接口
-
A BaseRelation that can eliminate unneeded columns before producing an RDD
containing all of its tuples as Row objects.
- Pseudorandom - org.apache.spark.util.random中的接口
-
:: DeveloperApi ::
A class with pseudorandom behavior.
- pushedFilters() - 接口 中的方法org.apache.spark.sql.connector.read.SupportsPushDownFilters
-
- pushFilters(Filter[]) - 接口 中的方法org.apache.spark.sql.connector.read.SupportsPushDownFilters
-
Pushes down filters, and returns filters that need to be evaluated after scanning.
- put(ParamPair<?>...) - 类 中的方法org.apache.spark.ml.param.ParamMap
-
Puts a list of param pairs (overwrites if the input params exists).
- put(Param<T>, T) - 类 中的方法org.apache.spark.ml.param.ParamMap
-
Puts a (param, value) pair (overwrites if the input param exists).
- put(Seq<ParamPair<?>>) - 类 中的方法org.apache.spark.ml.param.ParamMap
-
Puts a list of param pairs (overwrites if the input params exists).
- put(String, String) - 类 中的方法org.apache.spark.sql.util.CaseInsensitiveStringMap
-
- put(Object) - 类 中的方法org.apache.spark.util.sketch.BloomFilter
-
Puts an item into this BloomFilter.
- putAll(Map<? extends String, ? extends String>) - 类 中的方法org.apache.spark.sql.util.CaseInsensitiveStringMap
-
- putBinary(byte[]) - 类 中的方法org.apache.spark.util.sketch.BloomFilter
-
- putBoolean(String, boolean) - 类 中的方法org.apache.spark.sql.types.MetadataBuilder
-
Puts a Boolean.
- putBooleanArray(String, boolean[]) - 类 中的方法org.apache.spark.sql.types.MetadataBuilder
-
Puts a Boolean array.
- putDouble(String, double) - 类 中的方法org.apache.spark.sql.types.MetadataBuilder
-
Puts a Double.
- putDoubleArray(String, double[]) - 类 中的方法org.apache.spark.sql.types.MetadataBuilder
-
Puts a Double array.
- putLong(String, long) - 类 中的方法org.apache.spark.sql.types.MetadataBuilder
-
Puts a Long.
- putLong(long) - 类 中的方法org.apache.spark.util.sketch.BloomFilter
-
- putLongArray(String, long[]) - 类 中的方法org.apache.spark.sql.types.MetadataBuilder
-
Puts a Long array.
- putMetadata(String, Metadata) - 类 中的方法org.apache.spark.sql.types.MetadataBuilder
-
- putMetadataArray(String, Metadata[]) - 类 中的方法org.apache.spark.sql.types.MetadataBuilder
-
- putNull(String) - 类 中的方法org.apache.spark.sql.types.MetadataBuilder
-
Puts a null.
- putString(String, String) - 类 中的方法org.apache.spark.sql.types.MetadataBuilder
-
Puts a String.
- putString(String) - 类 中的方法org.apache.spark.util.sketch.BloomFilter
-
- putStringArray(String, String[]) - 类 中的方法org.apache.spark.sql.types.MetadataBuilder
-
Puts a String array.
- pValue() - 类 中的方法org.apache.spark.mllib.stat.test.ChiSqTestResult
-
- pValue() - 类 中的方法org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult
-
- pValue() - 接口 中的方法org.apache.spark.mllib.stat.test.TestResult
-
The probability of obtaining a test statistic result at least as extreme as the one that was
actually observed, assuming that the null hypothesis is true.
- pValues() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionTrainingSummary
-
- pValues() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionSummary
-
- PYSPARK_EXECUTOR_MEMORY() - 类 中的静态方法org.apache.spark.internal.config.Python
-
- Python - org.apache.spark.internal.config中的类
-
- Python() - 类 的构造器org.apache.spark.internal.config.Python
-
- PYTHON_DAEMON_MODULE() - 类 中的静态方法org.apache.spark.internal.config.Python
-
- PYTHON_TASK_KILL_TIMEOUT() - 类 中的静态方法org.apache.spark.internal.config.Python
-
- PYTHON_USE_DAEMON() - 类 中的静态方法org.apache.spark.internal.config.Python
-
- PYTHON_WORKER_MODULE() - 类 中的静态方法org.apache.spark.internal.config.Python
-
- PYTHON_WORKER_REUSE() - 类 中的静态方法org.apache.spark.internal.config.Python
-
- PythonStreamingListener - org.apache.spark.streaming.api.java中的接口
-
- pyUDT() - 类 中的方法org.apache.spark.mllib.linalg.VectorUDT
-
- R - org.apache.spark.internal.config中的类
-
- R() - 类 的构造器org.apache.spark.internal.config.R
-
- R() - 类 中的方法org.apache.spark.mllib.linalg.QRDecomposition
-
- r2() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionSummary
-
Returns R^2^, the coefficient of determination.
- r2() - 类 中的方法org.apache.spark.mllib.evaluation.RegressionMetrics
-
Returns R^2^, the unadjusted coefficient of determination.
- r2adj() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionSummary
-
Returns Adjusted R^2^, the adjusted coefficient of determination.
- R_BACKEND_CONNECTION_TIMEOUT() - 类 中的静态方法org.apache.spark.internal.config.R
-
- R_COMMAND() - 类 中的静态方法org.apache.spark.internal.config.R
-
- R_HEARTBEAT_INTERVAL() - 类 中的静态方法org.apache.spark.internal.config.R
-
- R_NUM_BACKEND_THREADS() - 类 中的静态方法org.apache.spark.internal.config.R
-
- RACK_LOCAL() - 类 中的静态方法org.apache.spark.scheduler.TaskLocality
-
- radians(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Converts an angle measured in degrees to an approximately equivalent angle measured in radians.
- radians(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Converts an angle measured in degrees to an approximately equivalent angle measured in radians.
- rand(int, int, Random) - 类 中的静态方法org.apache.spark.ml.linalg.DenseMatrix
-
Generate a DenseMatrix consisting of i.i.d.
- rand(int, int, Random) - 类 中的静态方法org.apache.spark.ml.linalg.Matrices
-
Generate a DenseMatrix consisting of i.i.d.
- rand(int, int, Random) - 类 中的静态方法org.apache.spark.mllib.linalg.DenseMatrix
-
Generate a DenseMatrix consisting of i.i.d.
- rand(int, int, Random) - 类 中的静态方法org.apache.spark.mllib.linalg.Matrices
-
Generate a DenseMatrix consisting of i.i.d.
- rand(long) - 类 中的静态方法org.apache.spark.sql.functions
-
Generate a random column with independent and identically distributed (i.i.d.) samples
from U[0.0, 1.0].
- rand() - 类 中的静态方法org.apache.spark.sql.functions
-
Generate a random column with independent and identically distributed (i.i.d.) samples
from U[0.0, 1.0].
- randn(int, int, Random) - 类 中的静态方法org.apache.spark.ml.linalg.DenseMatrix
-
Generate a DenseMatrix consisting of i.i.d.
- randn(int, int, Random) - 类 中的静态方法org.apache.spark.ml.linalg.Matrices
-
Generate a DenseMatrix consisting of i.i.d.
- randn(int, int, Random) - 类 中的静态方法org.apache.spark.mllib.linalg.DenseMatrix
-
Generate a DenseMatrix consisting of i.i.d.
- randn(int, int, Random) - 类 中的静态方法org.apache.spark.mllib.linalg.Matrices
-
Generate a DenseMatrix consisting of i.i.d.
- randn(long) - 类 中的静态方法org.apache.spark.sql.functions
-
Generate a column with independent and identically distributed (i.i.d.) samples from
the standard normal distribution.
- randn() - 类 中的静态方法org.apache.spark.sql.functions
-
Generate a column with independent and identically distributed (i.i.d.) samples from
the standard normal distribution.
- random() - 类 中的方法org.apache.spark.ml.image.SamplePathFilter
-
- RANDOM() - 类 中的静态方法org.apache.spark.mllib.clustering.KMeans
-
- random() - 类 中的静态方法org.apache.spark.util.Utils
-
- RandomBlockReplicationPolicy - org.apache.spark.storage中的类
-
- RandomBlockReplicationPolicy() - 类 的构造器org.apache.spark.storage.RandomBlockReplicationPolicy
-
- RandomDataGenerator<T> - org.apache.spark.mllib.random中的接口
-
:: DeveloperApi ::
Trait for random data generators that generate i.i.d. data.
- RandomForest - org.apache.spark.ml.tree.impl中的类
-
ALGORITHM
This is a sketch of the algorithm to help new developers.
- RandomForest() - 类 的构造器org.apache.spark.ml.tree.impl.RandomForest
-
- RandomForest - org.apache.spark.mllib.tree中的类
-
A class that implements a
Random Forest
learning algorithm for classification and regression.
- RandomForest(Strategy, int, String, int) - 类 的构造器org.apache.spark.mllib.tree.RandomForest
-
- RandomForestClassificationModel - org.apache.spark.ml.classification中的类
-
- RandomForestClassifier - org.apache.spark.ml.classification中的类
-
- RandomForestClassifier(String) - 类 的构造器org.apache.spark.ml.classification.RandomForestClassifier
-
- RandomForestClassifier() - 类 的构造器org.apache.spark.ml.classification.RandomForestClassifier
-
- RandomForestClassifierParams - org.apache.spark.ml.tree中的接口
-
- RandomForestModel - org.apache.spark.mllib.tree.model中的类
-
Represents a random forest model.
- RandomForestModel(Enumeration.Value, DecisionTreeModel[]) - 类 的构造器org.apache.spark.mllib.tree.model.RandomForestModel
-
- RandomForestParams - org.apache.spark.ml.tree中的接口
-
Parameters for Random Forest algorithms.
- RandomForestRegressionModel - org.apache.spark.ml.regression中的类
-
- RandomForestRegressor - org.apache.spark.ml.regression中的类
-
- RandomForestRegressor(String) - 类 的构造器org.apache.spark.ml.regression.RandomForestRegressor
-
- RandomForestRegressor() - 类 的构造器org.apache.spark.ml.regression.RandomForestRegressor
-
- RandomForestRegressorParams - org.apache.spark.ml.tree中的接口
-
- randomize(TraversableOnce<T>, ClassTag<T>) - 类 中的静态方法org.apache.spark.util.Utils
-
Shuffle the elements of a collection into a random order, returning the
result in a new collection.
- randomizeInPlace(Object, Random) - 类 中的静态方法org.apache.spark.util.Utils
-
Shuffle the elements of an array into a random order, modifying the
original array.
- randomJavaRDD(JavaSparkContext, RandomDataGenerator<T>, long, int, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
:: DeveloperApi ::
Generates an RDD comprised of i.i.d.
- randomJavaRDD(JavaSparkContext, RandomDataGenerator<T>, long, int) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
:: DeveloperApi ::
RandomRDDs.randomJavaRDD with the default seed.
- randomJavaRDD(JavaSparkContext, RandomDataGenerator<T>, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
:: DeveloperApi ::
RandomRDDs.randomJavaRDD with the default seed & numPartitions
- randomJavaVectorRDD(JavaSparkContext, RandomDataGenerator<Object>, long, int, int, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
:: DeveloperApi ::
Java-friendly version of RandomRDDs.randomVectorRDD.
- randomJavaVectorRDD(JavaSparkContext, RandomDataGenerator<Object>, long, int, int) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
:: DeveloperApi ::
RandomRDDs.randomJavaVectorRDD with the default seed.
- randomJavaVectorRDD(JavaSparkContext, RandomDataGenerator<Object>, long, int) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
:: DeveloperApi ::
RandomRDDs.randomJavaVectorRDD with the default number of partitions and the default seed.
- randomRDD(SparkContext, RandomDataGenerator<T>, long, int, long, ClassTag<T>) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
:: DeveloperApi ::
Generates an RDD comprised of i.i.d.
- RandomRDDs - org.apache.spark.mllib.random中的类
-
Generator methods for creating RDDs comprised of i.i.d.
- RandomRDDs() - 类 的构造器org.apache.spark.mllib.random.RandomRDDs
-
- RandomSampler<T,U> - org.apache.spark.util.random中的接口
-
:: DeveloperApi ::
A pseudorandom sampler.
- randomSplit(double[]) - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Randomly splits this RDD with the provided weights.
- randomSplit(double[], long) - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Randomly splits this RDD with the provided weights.
- randomSplit(double[], long) - 类 中的方法org.apache.spark.rdd.RDD
-
Randomly splits this RDD with the provided weights.
- randomSplit(double[], long) - 类 中的方法org.apache.spark.sql.Dataset
-
Randomly splits this Dataset with the provided weights.
- randomSplit(double[]) - 类 中的方法org.apache.spark.sql.Dataset
-
Randomly splits this Dataset with the provided weights.
- randomSplitAsList(double[], long) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a Java list that contains randomly split Dataset with the provided weights.
- randomVectorRDD(SparkContext, RandomDataGenerator<Object>, long, int, int, long) - 类 中的静态方法org.apache.spark.mllib.random.RandomRDDs
-
:: DeveloperApi ::
Generates an RDD[Vector] with vectors containing i.i.d.
- RandomVertexCut$() - 类 的构造器org.apache.spark.graphx.PartitionStrategy.RandomVertexCut$
-
- range() - 类 中的方法org.apache.spark.ml.feature.RobustScalerModel
-
- range(long, long, long, int) - 类 中的方法org.apache.spark.SparkContext
-
Creates a new RDD[Long] containing elements from start to end(exclusive), increased by
step every element.
- range(long) - 类 中的方法org.apache.spark.sql.SparkSession
-
Creates a
Dataset with a single
LongType column named
id, containing elements
in a range from 0 to
end (exclusive) with step value 1.
- range(long, long) - 类 中的方法org.apache.spark.sql.SparkSession
-
Creates a
Dataset with a single
LongType column named
id, containing elements
in a range from
start to
end (exclusive) with step value 1.
- range(long, long, long) - 类 中的方法org.apache.spark.sql.SparkSession
-
Creates a
Dataset with a single
LongType column named
id, containing elements
in a range from
start to
end (exclusive) with a step value.
- range(long, long, long, int) - 类 中的方法org.apache.spark.sql.SparkSession
-
Creates a
Dataset with a single
LongType column named
id, containing elements
in a range from
start to
end (exclusive) with a step value, with partition number
specified.
- range(long) - 类 中的方法org.apache.spark.sql.SQLContext
-
Creates a DataFrame with a single LongType column named id, containing elements
in a range from 0 to end (exclusive) with step value 1.
- range(long, long) - 类 中的方法org.apache.spark.sql.SQLContext
-
Creates a DataFrame with a single LongType column named id, containing elements
in a range from start to end (exclusive) with step value 1.
- range(long, long, long) - 类 中的方法org.apache.spark.sql.SQLContext
-
Creates a DataFrame with a single LongType column named id, containing elements
in a range from start to end (exclusive) with a step value.
- range(long, long, long, int) - 类 中的方法org.apache.spark.sql.SQLContext
-
Creates a DataFrame with a single LongType column named id, containing elements
in an range from start to end (exclusive) with an step value, with partition number
specified.
- rangeBetween(long, long) - 类 中的静态方法org.apache.spark.sql.expressions.Window
-
Creates a
WindowSpec with the frame boundaries defined,
from
start (inclusive) to
end (inclusive).
- rangeBetween(long, long) - 类 中的方法org.apache.spark.sql.expressions.WindowSpec
-
Defines the frame boundaries, from start (inclusive) to end (inclusive).
- RangeDependency<T> - org.apache.spark中的类
-
:: DeveloperApi ::
Represents a one-to-one dependency between ranges of partitions in the parent and child RDDs.
- RangeDependency(RDD<T>, int, int, int) - 类 的构造器org.apache.spark.RangeDependency
-
- RangePartitioner<K,V> - org.apache.spark中的类
-
A
Partitioner that partitions sortable records by range into roughly
equal ranges.
- RangePartitioner(int, RDD<? extends Product2<K, V>>, boolean, int, Ordering<K>, ClassTag<K>) - 类 的构造器org.apache.spark.RangePartitioner
-
- RangePartitioner(int, RDD<? extends Product2<K, V>>, boolean, Ordering<K>, ClassTag<K>) - 类 的构造器org.apache.spark.RangePartitioner
-
- rank() - 类 中的方法org.apache.spark.graphx.lib.SVDPlusPlus.Conf
-
- rank() - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- rank() - 类 中的方法org.apache.spark.ml.recommendation.ALSModel
-
- rank() - 接口 中的方法org.apache.spark.ml.recommendation.ALSParams
-
Param for rank of the matrix factorization (positive).
- rank() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary
-
- rank() - 类 中的方法org.apache.spark.mllib.recommendation.MatrixFactorizationModel
-
- rank() - 类 中的静态方法org.apache.spark.sql.functions
-
Window function: returns the rank of rows within a window partition.
- RankingEvaluator - org.apache.spark.ml.evaluation中的类
-
:: Experimental ::
Evaluator for ranking, which expects two input columns: prediction and label.
- RankingEvaluator(String) - 类 的构造器org.apache.spark.ml.evaluation.RankingEvaluator
-
- RankingEvaluator() - 类 的构造器org.apache.spark.ml.evaluation.RankingEvaluator
-
- RankingMetrics<T> - org.apache.spark.mllib.evaluation中的类
-
Evaluator for ranking algorithms.
- RankingMetrics(RDD<Tuple2<Object, Object>>, ClassTag<T>) - 类 的构造器org.apache.spark.mllib.evaluation.RankingMetrics
-
- RateEstimator - org.apache.spark.streaming.scheduler.rate中的接口
-
A component that estimates the rate at which an InputDStream should ingest
records, based on updates at every batch completion.
- Rating(ID, ID, float) - 类 的构造器org.apache.spark.ml.recommendation.ALS.Rating
-
- rating() - 类 中的方法org.apache.spark.ml.recommendation.ALS.Rating
-
- Rating - org.apache.spark.mllib.recommendation中的类
-
A more compact class to represent a rating than Tuple3[Int, Int, Double].
- Rating(int, int, double) - 类 的构造器org.apache.spark.mllib.recommendation.Rating
-
- rating() - 类 中的方法org.apache.spark.mllib.recommendation.Rating
-
- Rating$() - 类 的构造器org.apache.spark.ml.recommendation.ALS.Rating$
-
- RatingBlock$() - 类 的构造器org.apache.spark.ml.recommendation.ALS.RatingBlock$
-
- ratingCol() - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- ratingCol() - 接口 中的方法org.apache.spark.ml.recommendation.ALSParams
-
Param for the column name for ratings.
- ratioParam() - 类 中的静态方法org.apache.spark.ml.image.SamplePathFilter
-
- raw2ProbabilityInPlace(Vector) - 接口 中的方法org.apache.spark.ml.ann.TopologyModel
-
Probability of the model.
- rawCount() - 类 中的方法org.apache.spark.ml.tree.DecisionTreeModelReadWrite.NodeData
-
- rawPredictionCol() - 类 中的方法org.apache.spark.ml.classification.ClassificationModel
-
- rawPredictionCol() - 类 中的方法org.apache.spark.ml.classification.Classifier
-
- rawPredictionCol() - 类 中的方法org.apache.spark.ml.classification.OneVsRest
-
- rawPredictionCol() - 类 中的方法org.apache.spark.ml.classification.OneVsRestModel
-
- rawPredictionCol() - 类 中的方法org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
-
- rawPredictionCol() - 接口 中的方法org.apache.spark.ml.param.shared.HasRawPredictionCol
-
Param for raw prediction (a.k.a. confidence) column name.
- rawSocketStream(String, int, StorageLevel) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Create an input stream from network source hostname:port, where data is received
as serialized blocks (serialized using the Spark's serializer) that can be directly
pushed into the block manager without deserializing them.
- rawSocketStream(String, int) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Create an input stream from network source hostname:port, where data is received
as serialized blocks (serialized using the Spark's serializer) that can be directly
pushed into the block manager without deserializing them.
- rawSocketStream(String, int, StorageLevel, ClassTag<T>) - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Create an input stream from network source hostname:port, where data is received
as serialized blocks (serialized using the Spark's serializer) that can be directly
pushed into the block manager without deserializing them.
- RawTextHelper - org.apache.spark.streaming.util中的类
-
- RawTextHelper() - 类 的构造器org.apache.spark.streaming.util.RawTextHelper
-
- RawTextSender - org.apache.spark.streaming.util中的类
-
A helper program that sends blocks of Kryo-serialized text strings out on a socket at a
specified rate.
- RawTextSender() - 类 的构造器org.apache.spark.streaming.util.RawTextSender
-
- RBackendAuthHandler - org.apache.spark.api.r中的类
-
Authentication handler for connections from the R process.
- RBackendAuthHandler(String) - 类 的构造器org.apache.spark.api.r.RBackendAuthHandler
-
- rdd() - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
- rdd() - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
- rdd() - 类 中的方法org.apache.spark.api.java.JavaRDD
-
- rdd() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
- RDD() - 类 中的静态方法org.apache.spark.api.r.RRunnerModes
-
- rdd() - 类 中的方法org.apache.spark.Dependency
-
- rdd() - 类 中的方法org.apache.spark.NarrowDependency
-
- RDD<T> - org.apache.spark.rdd中的类
-
A Resilient Distributed Dataset (RDD), the basic abstraction in Spark.
- RDD(SparkContext, Seq<Dependency<?>>, ClassTag<T>) - 类 的构造器org.apache.spark.rdd.RDD
-
- RDD(RDD<?>, ClassTag<T>) - 类 的构造器org.apache.spark.rdd.RDD
-
Construct an RDD with just a one-to-one dependency on one parent
- rdd() - 类 中的方法org.apache.spark.ShuffleDependency
-
- rdd() - 类 中的方法org.apache.spark.sql.Dataset
-
- RDD() - 类 中的静态方法org.apache.spark.storage.BlockId
-
- RDD_NAME() - 类 中的静态方法org.apache.spark.ui.storage.ToolTips
-
- RDDBarrier<T> - org.apache.spark.rdd中的类
-
:: Experimental ::
Wraps an RDD in a barrier stage, which forces Spark to launch tasks of this stage together.
- RDDBlockId - org.apache.spark.storage中的类
-
- RDDBlockId(int, int) - 类 的构造器org.apache.spark.storage.RDDBlockId
-
- rddBlocks() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- rddBlocks() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- rddCleaned(int) - 接口 中的方法org.apache.spark.CleanerListener
-
- RDDDataDistribution - org.apache.spark.status.api.v1中的类
-
- RDDFunctions<T> - org.apache.spark.mllib.rdd中的类
-
:: DeveloperApi ::
Machine learning specific RDD functions.
- RDDFunctions(RDD<T>, ClassTag<T>) - 类 的构造器org.apache.spark.mllib.rdd.RDDFunctions
-
- rddId() - 类 中的方法org.apache.spark.CleanCheckpoint
-
- rddId() - 类 中的方法org.apache.spark.CleanRDD
-
- rddId() - 类 中的方法org.apache.spark.scheduler.SparkListenerUnpersistRDD
-
- rddId() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.RemoveRdd
-
- rddId() - 类 中的方法org.apache.spark.storage.RDDBlockId
-
- rddIds() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- RDDInfo - org.apache.spark.storage中的类
-
- RDDInfo(int, String, int, StorageLevel, boolean, Seq<Object>, String, Option<org.apache.spark.rdd.RDDOperationScope>) - 类 的构造器org.apache.spark.storage.RDDInfo
-
- rddInfoFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- rddInfos() - 类 中的方法org.apache.spark.scheduler.StageInfo
-
- rddInfoToJson(RDDInfo) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- RDDPartitionInfo - org.apache.spark.status.api.v1中的类
-
- RDDPartitionSeq - org.apache.spark.status中的类
-
A custom sequence of partitions based on a mutable linked list.
- RDDPartitionSeq() - 类 的构造器org.apache.spark.status.RDDPartitionSeq
-
- rdds() - 类 中的方法org.apache.spark.rdd.CoGroupedRDD
-
- rdds() - 类 中的方法org.apache.spark.rdd.UnionRDD
-
- RDDStorageInfo - org.apache.spark.status.api.v1中的类
-
- rddToAsyncRDDActions(RDD<T>, ClassTag<T>) - 类 中的静态方法org.apache.spark.rdd.RDD
-
- rddToDatasetHolder(RDD<T>, Encoder<T>) - 类 中的方法org.apache.spark.sql.SQLImplicits
-
- rddToOrderedRDDFunctions(RDD<Tuple2<K, V>>, Ordering<K>, ClassTag<K>, ClassTag<V>) - 类 中的静态方法org.apache.spark.rdd.RDD
-
- rddToPairRDDFunctions(RDD<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>, Ordering<K>) - 类 中的静态方法org.apache.spark.rdd.RDD
-
- rddToSequenceFileRDDFunctions(RDD<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>, <any>, <any>) - 类 中的静态方法org.apache.spark.rdd.RDD
-
- read() - 类 中的方法org.apache.spark.io.NioBufferedFileInputStream
-
- read(byte[], int, int) - 类 中的方法org.apache.spark.io.NioBufferedFileInputStream
-
- read() - 类 中的方法org.apache.spark.io.ReadAheadInputStream
-
- read(byte[], int, int) - 类 中的方法org.apache.spark.io.ReadAheadInputStream
-
- read() - 类 中的静态方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- read() - 类 中的静态方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- read() - 类 中的静态方法org.apache.spark.ml.classification.GBTClassificationModel
-
- read() - 类 中的静态方法org.apache.spark.ml.classification.GBTClassifier
-
- read() - 类 中的静态方法org.apache.spark.ml.classification.LinearSVC
-
- read() - 类 中的静态方法org.apache.spark.ml.classification.LinearSVCModel
-
- read() - 类 中的静态方法org.apache.spark.ml.classification.LogisticRegression
-
- read() - 类 中的静态方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- read() - 类 中的静态方法org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel
-
- read() - 类 中的静态方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
- read() - 类 中的静态方法org.apache.spark.ml.classification.NaiveBayes
-
- read() - 类 中的静态方法org.apache.spark.ml.classification.NaiveBayesModel
-
- read() - 类 中的静态方法org.apache.spark.ml.classification.OneVsRest
-
- read() - 类 中的静态方法org.apache.spark.ml.classification.OneVsRestModel
-
- read() - 类 中的静态方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- read() - 类 中的静态方法org.apache.spark.ml.classification.RandomForestClassifier
-
- read() - 类 中的静态方法org.apache.spark.ml.clustering.BisectingKMeans
-
- read() - 类 中的静态方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
- read() - 类 中的静态方法org.apache.spark.ml.clustering.DistributedLDAModel
-
- read() - 类 中的静态方法org.apache.spark.ml.clustering.GaussianMixture
-
- read() - 类 中的静态方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- read() - 类 中的静态方法org.apache.spark.ml.clustering.KMeans
-
- read() - 类 中的静态方法org.apache.spark.ml.clustering.KMeansModel
-
- read() - 类 中的静态方法org.apache.spark.ml.clustering.LDA
-
- read() - 类 中的静态方法org.apache.spark.ml.clustering.LocalLDAModel
-
- read() - 类 中的静态方法org.apache.spark.ml.clustering.PowerIterationClustering
-
- read() - 类 中的静态方法org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
-
- read() - 类 中的静态方法org.apache.spark.ml.evaluation.ClusteringEvaluator
-
- read() - 类 中的静态方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- read() - 类 中的静态方法org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator
-
- read() - 类 中的静态方法org.apache.spark.ml.evaluation.RankingEvaluator
-
- read() - 类 中的静态方法org.apache.spark.ml.evaluation.RegressionEvaluator
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.Binarizer
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.BucketedRandomProjectionLSH
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.BucketedRandomProjectionLSHModel
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.Bucketizer
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.ChiSqSelector
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.ChiSqSelectorModel
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.ColumnPruner
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.CountVectorizer
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.CountVectorizerModel
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.DCT
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.ElementwiseProduct
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.FeatureHasher
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.HashingTF
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.IDF
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.IDFModel
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.Imputer
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.ImputerModel
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.IndexToString
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.Interaction
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.MaxAbsScaler
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.MaxAbsScalerModel
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.MinHashLSH
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.MinHashLSHModel
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.MinMaxScaler
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.MinMaxScalerModel
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.NGram
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.Normalizer
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.OneHotEncoder
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.OneHotEncoderModel
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.PCA
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.PCAModel
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.PolynomialExpansion
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.QuantileDiscretizer
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.RegexTokenizer
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.RFormula
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.RFormulaModel
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.RobustScaler
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.RobustScalerModel
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.SQLTransformer
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.StandardScaler
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.StandardScalerModel
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.StopWordsRemover
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.StringIndexer
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.StringIndexerModel
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.Tokenizer
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.VectorAssembler
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.VectorAttributeRewriter
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.VectorIndexer
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.VectorIndexerModel
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.VectorSizeHint
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.VectorSlicer
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.Word2Vec
-
- read() - 类 中的静态方法org.apache.spark.ml.feature.Word2VecModel
-
- read() - 类 中的静态方法org.apache.spark.ml.fpm.FPGrowth
-
- read() - 类 中的静态方法org.apache.spark.ml.fpm.FPGrowthModel
-
- read() - 类 中的静态方法org.apache.spark.ml.Pipeline
-
- read() - 类 中的静态方法org.apache.spark.ml.PipelineModel
-
- read() - 类 中的静态方法org.apache.spark.ml.recommendation.ALS
-
- read() - 类 中的静态方法org.apache.spark.ml.recommendation.ALSModel
-
- read() - 类 中的静态方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- read() - 类 中的静态方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- read() - 类 中的静态方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- read() - 类 中的静态方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- read() - 类 中的静态方法org.apache.spark.ml.regression.GBTRegressionModel
-
- read() - 类 中的静态方法org.apache.spark.ml.regression.GBTRegressor
-
- read() - 类 中的静态方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
- read() - 类 中的静态方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
- read() - 类 中的静态方法org.apache.spark.ml.regression.IsotonicRegression
-
- read() - 类 中的静态方法org.apache.spark.ml.regression.IsotonicRegressionModel
-
- read() - 类 中的静态方法org.apache.spark.ml.regression.LinearRegression
-
- read() - 类 中的静态方法org.apache.spark.ml.regression.LinearRegressionModel
-
- read() - 类 中的静态方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- read() - 类 中的静态方法org.apache.spark.ml.regression.RandomForestRegressor
-
- read() - 类 中的静态方法org.apache.spark.ml.tuning.CrossValidator
-
- read() - 类 中的静态方法org.apache.spark.ml.tuning.CrossValidatorModel
-
- read() - 类 中的静态方法org.apache.spark.ml.tuning.TrainValidationSplit
-
- read() - 类 中的静态方法org.apache.spark.ml.tuning.TrainValidationSplitModel
-
- read() - 接口 中的方法org.apache.spark.ml.util.DefaultParamsReadable
-
- read() - 接口 中的方法org.apache.spark.ml.util.MLReadable
-
Returns an MLReader instance for this class.
- read(ByteBuffer) - 类 中的方法org.apache.spark.security.CryptoStreamUtils.ErrorHandlingReadableChannel
-
- read(Kryo, Input, Class<Iterable<?>>) - 类 中的方法org.apache.spark.serializer.JavaIterableWrapperSerializer
-
- read() - 类 中的方法org.apache.spark.sql.SparkSession
-
Returns a
DataFrameReader that can be used to read non-streaming data in as a
DataFrame.
- read() - 类 中的方法org.apache.spark.sql.SQLContext
-
Returns a
DataFrameReader that can be used to read non-streaming data in as a
DataFrame.
- read() - 类 中的方法org.apache.spark.storage.BufferReleasingInputStream
-
- read(byte[]) - 类 中的方法org.apache.spark.storage.BufferReleasingInputStream
-
- read(byte[], int, int) - 类 中的方法org.apache.spark.storage.BufferReleasingInputStream
-
- read(String) - 类 中的静态方法org.apache.spark.streaming.CheckpointReader
-
Read checkpoint files present in the given checkpoint directory.
- read(String, SparkConf, Configuration, boolean) - 类 中的静态方法org.apache.spark.streaming.CheckpointReader
-
Read checkpoint files present in the given checkpoint directory.
- read(WriteAheadLogRecordHandle) - 类 中的方法org.apache.spark.streaming.util.WriteAheadLog
-
Read a written record based on the given record handle.
- ReadableChannelFileRegion - org.apache.spark.storage中的类
-
- ReadableChannelFileRegion(ReadableByteChannel, long) - 类 的构造器org.apache.spark.storage.ReadableChannelFileRegion
-
- ReadAheadInputStream - org.apache.spark.io中的类
-
InputStream implementation which asynchronously reads ahead from the underlying input
stream when specified amount of data has been read from the current buffer.
- ReadAheadInputStream(InputStream, int) - 类 的构造器org.apache.spark.io.ReadAheadInputStream
-
Creates a ReadAheadInputStream with the specified buffer size and read-ahead
threshold
- readAll() - 类 中的方法org.apache.spark.streaming.util.WriteAheadLog
-
Read and return an iterator of all the records that have been written but not yet cleaned up.
- readArray(DataInputStream, JVMObjectTracker) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readArrowStreamFromFile(SparkSession, String) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
R callable function to read a file in Arrow stream format and create an RDD
using each serialized ArrowRecordBatch as a partition.
- readBoolean(DataInputStream) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readBooleanArr(DataInputStream) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readBytes(DataInputStream) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readBytes() - 类 中的方法org.apache.spark.status.api.v1.ShuffleReadMetricDistributions
-
- readBytesArr(DataInputStream) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readDate(DataInputStream) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readDouble(DataInputStream) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readDoubleArr(DataInputStream) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- reader() - 类 中的方法org.apache.spark.ml.LoadInstanceEnd
-
- reader() - 类 中的方法org.apache.spark.ml.LoadInstanceStart
-
- readExternal(ObjectInput) - 类 中的方法org.apache.spark.serializer.JavaSerializer
-
- readExternal(ObjectInput) - 类 中的方法org.apache.spark.storage.BlockManagerId
-
- readExternal(ObjectInput) - 类 中的方法org.apache.spark.storage.BlockManagerMessages.UpdateBlockInfo
-
- readExternal(ObjectInput) - 类 中的方法org.apache.spark.storage.StorageLevel
-
- readFrom(ConfigReader) - 类 中的方法org.apache.spark.internal.config.ConfigEntryWithDefault
-
- readFrom(ConfigReader) - 类 中的方法org.apache.spark.internal.config.ConfigEntryWithDefaultFunction
-
- readFrom(ConfigReader) - 类 中的方法org.apache.spark.internal.config.ConfigEntryWithDefaultString
-
- readFrom(InputStream) - 类 中的静态方法org.apache.spark.util.sketch.BloomFilter
-
- readFrom(InputStream) - 类 中的静态方法org.apache.spark.util.sketch.CountMinSketch
-
- readFrom(byte[]) - 类 中的静态方法org.apache.spark.util.sketch.CountMinSketch
-
- readInt(DataInputStream) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readIntArr(DataInputStream) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readKey(ClassTag<T>) - 类 中的方法org.apache.spark.serializer.DeserializationStream
-
Reads the object representing the key of a key-value pair.
- readList(DataInputStream, JVMObjectTracker) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readMap(DataInputStream, JVMObjectTracker) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readObject(DataInputStream, JVMObjectTracker) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readObject(ClassTag<T>) - 类 中的方法org.apache.spark.serializer.DeserializationStream
-
The most general-purpose method to read an object.
- readObjectType(DataInputStream) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readOrcSchemasInParallel(Seq<FileStatus>, Configuration, boolean) - 类 中的静态方法org.apache.spark.sql.hive.orc.OrcFileOperator
-
Reads ORC file schemas in multi-threaded manner, using Hive ORC library.
- readRecords() - 类 中的方法org.apache.spark.status.api.v1.ShuffleReadMetricDistributions
-
- readSchema() - 接口 中的方法org.apache.spark.sql.connector.read.Scan
-
Returns the actual schema of this data source scan, which may be different from the physical
schema of the underlying storage, as column pruning or other optimizations may happen.
- readSchema(Seq<String>, Option<Configuration>, boolean) - 类 中的静态方法org.apache.spark.sql.hive.orc.OrcFileOperator
-
- readSqlObject(DataInputStream, char) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
- readStream() - 类 中的方法org.apache.spark.sql.SparkSession
-
Returns a DataStreamReader that can be used to read streaming data in as a DataFrame.
- readStream() - 类 中的方法org.apache.spark.sql.SQLContext
-
Returns a DataStreamReader that can be used to read streaming data in as a DataFrame.
- readString(DataInputStream) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readStringArr(DataInputStream) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readStringBytes(DataInputStream, int) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readTime(DataInputStream) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readTypedObject(DataInputStream, char, JVMObjectTracker) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- readValue(ClassTag<T>) - 类 中的方法org.apache.spark.serializer.DeserializationStream
-
Reads the object representing the value of a key-value pair.
- ready(Duration, CanAwait) - 类 中的方法org.apache.spark.ComplexFutureAction
-
- ready(Duration, CanAwait) - 接口 中的方法org.apache.spark.FutureAction
-
Blocks until this action completes.
- ready(Duration, CanAwait) - 类 中的方法org.apache.spark.SimpleFutureAction
-
- REAPER_ITERATIONS() - 类 中的静态方法org.apache.spark.internal.config.Deploy
-
- reason() - 类 中的方法org.apache.spark.ExecutorLostFailure
-
- reason() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.KillTask
-
- reason() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RemoveExecutor
-
- reason() - 类 中的方法org.apache.spark.scheduler.local.KillTask
-
- reason() - 类 中的方法org.apache.spark.scheduler.SparkListenerExecutorRemoved
-
- reason() - 类 中的方法org.apache.spark.scheduler.SparkListenerTaskEnd
-
- reason() - 类 中的方法org.apache.spark.TaskKilled
-
- reason() - 异常错误 中的方法org.apache.spark.TaskKilledException
-
- Recall - org.apache.spark.mllib.evaluation.binary中的类
-
Recall.
- Recall() - 类 的构造器org.apache.spark.mllib.evaluation.binary.Recall
-
- recall(double) - 类 中的方法org.apache.spark.mllib.evaluation.MulticlassMetrics
-
Returns recall for a given label (category)
- recall() - 类 中的方法org.apache.spark.mllib.evaluation.MultilabelMetrics
-
Returns document-based recall averaged by the number of documents
- recall(double) - 类 中的方法org.apache.spark.mllib.evaluation.MultilabelMetrics
-
Returns recall for a given label (category)
- recallAt(int) - 类 中的方法org.apache.spark.mllib.evaluation.RankingMetrics
-
Compute the average recall of all the queries, truncated at ranking position k.
- recallByLabel() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionSummary
-
Returns recall for each label (category).
- recallByThreshold() - 接口 中的方法org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
-
Returns a dataframe with two fields (threshold, recall) curve.
- recallByThreshold() - 类 中的方法org.apache.spark.ml.classification.BinaryLogisticRegressionSummaryImpl
-
- recallByThreshold() - 类 中的方法org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
-
Returns the (threshold, recall) curve.
- receive() - 接口 中的方法org.apache.spark.rpc.RpcEndpoint
-
Process messages from RpcEndpointRef.send or RpcCallContext.reply.
- receiveAndReply(RpcCallContext) - 接口 中的方法org.apache.spark.rpc.RpcEndpoint
-
Process messages from RpcEndpointRef.ask.
- ReceivedBlock - org.apache.spark.streaming.receiver中的接口
-
Trait representing a received block
- ReceivedBlockHandler - org.apache.spark.streaming.receiver中的接口
-
Trait that represents a class that handles the storage of blocks received by receiver
- ReceivedBlockStoreResult - org.apache.spark.streaming.receiver中的接口
-
Trait that represents the metadata related to storage of blocks
- ReceivedBlockTrackerLogEvent - org.apache.spark.streaming.scheduler中的接口
-
Trait representing any event in the ReceivedBlockTracker that updates its state.
- Receiver<T> - org.apache.spark.streaming.receiver中的类
-
:: DeveloperApi ::
Abstract class of a receiver that can be run on worker nodes to receive external data.
- Receiver(StorageLevel) - 类 的构造器org.apache.spark.streaming.receiver.Receiver
-
- RECEIVER_WAL_CLASS_CONF_KEY() - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- RECEIVER_WAL_CLOSE_AFTER_WRITE_CONF_KEY() - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- RECEIVER_WAL_ENABLE_CONF_KEY() - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- RECEIVER_WAL_MAX_FAILURES_CONF_KEY() - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- RECEIVER_WAL_ROLLING_INTERVAL_CONF_KEY() - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- ReceiverInfo - org.apache.spark.status.api.v1.streaming中的类
-
- ReceiverInfo - org.apache.spark.streaming.scheduler中的类
-
:: DeveloperApi ::
Class having information about a receiver
- ReceiverInfo(int, String, boolean, String, String, String, String, long) - 类 的构造器org.apache.spark.streaming.scheduler.ReceiverInfo
-
- receiverInfo() - 类 中的方法org.apache.spark.streaming.scheduler.StreamingListenerReceiverError
-
- receiverInfo() - 类 中的方法org.apache.spark.streaming.scheduler.StreamingListenerReceiverStarted
-
- receiverInfo() - 类 中的方法org.apache.spark.streaming.scheduler.StreamingListenerReceiverStopped
-
- receiverInputDStream() - 类 中的方法org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream
-
- receiverInputDStream() - 类 中的方法org.apache.spark.streaming.api.java.JavaReceiverInputDStream
-
- ReceiverInputDStream<T> - org.apache.spark.streaming.dstream中的类
-
Abstract class for defining any
InputDStream
that has to start a receiver on worker nodes to receive external data.
- ReceiverInputDStream(StreamingContext, ClassTag<T>) - 类 的构造器org.apache.spark.streaming.dstream.ReceiverInputDStream
-
- ReceiverMessage - org.apache.spark.streaming.receiver中的接口
-
Messages sent to the Receiver.
- ReceiverState - org.apache.spark.streaming.scheduler中的类
-
Enumeration to identify current state of a Receiver
- ReceiverState() - 类 的构造器org.apache.spark.streaming.scheduler.ReceiverState
-
- receiverStream(Receiver<T>) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Create an input stream with any arbitrary user implemented receiver.
- receiverStream(Receiver<T>, ClassTag<T>) - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Create an input stream with any arbitrary user implemented receiver.
- ReceiverTrackerLocalMessage - org.apache.spark.streaming.scheduler中的接口
-
Messages used by the driver and ReceiverTrackerEndpoint to communicate locally.
- ReceiverTrackerMessage - org.apache.spark.streaming.scheduler中的接口
-
Messages used by the NetworkReceiver and the ReceiverTracker to communicate
with each other.
- recentProgress() - 接口 中的方法org.apache.spark.sql.streaming.StreamingQuery
-
- recommendForAllItems(int) - 类 中的方法org.apache.spark.ml.recommendation.ALSModel
-
Returns top numUsers users recommended for each item, for all items.
- recommendForAllUsers(int) - 类 中的方法org.apache.spark.ml.recommendation.ALSModel
-
Returns top numItems items recommended for each user, for all users.
- recommendForItemSubset(Dataset<?>, int) - 类 中的方法org.apache.spark.ml.recommendation.ALSModel
-
Returns top numUsers users recommended for each item id in the input data set.
- recommendForUserSubset(Dataset<?>, int) - 类 中的方法org.apache.spark.ml.recommendation.ALSModel
-
Returns top numItems items recommended for each user id in the input data set.
- recommendProducts(int, int) - 类 中的方法org.apache.spark.mllib.recommendation.MatrixFactorizationModel
-
Recommends products to a user.
- recommendProductsForUsers(int) - 类 中的方法org.apache.spark.mllib.recommendation.MatrixFactorizationModel
-
Recommends top products for all users.
- recommendUsers(int, int) - 类 中的方法org.apache.spark.mllib.recommendation.MatrixFactorizationModel
-
Recommends users to a product.
- recommendUsersForProducts(int) - 类 中的方法org.apache.spark.mllib.recommendation.MatrixFactorizationModel
-
Recommends top users for all products.
- recordReader(InputStream, Configuration) - 类 中的方法org.apache.spark.sql.hive.execution.HiveScriptIOSchema
-
- recordReaderClass() - 类 中的方法org.apache.spark.sql.hive.execution.HiveScriptIOSchema
-
- RECORDS_BETWEEN_BYTES_READ_METRIC_UPDATES() - 类 中的静态方法org.apache.spark.rdd.HadoopRDD
-
Update the input bytes read metric each time this number of records has been read
- RECORDS_READ() - 类 中的方法org.apache.spark.InternalAccumulator.input$
-
- RECORDS_READ() - 类 中的方法org.apache.spark.InternalAccumulator.shuffleRead$
-
- RECORDS_WRITTEN() - 类 中的方法org.apache.spark.InternalAccumulator.output$
-
- RECORDS_WRITTEN() - 类 中的方法org.apache.spark.InternalAccumulator.shuffleWrite$
-
- recordsRead() - 类 中的方法org.apache.spark.status.api.v1.InputMetricDistributions
-
- recordsRead() - 类 中的方法org.apache.spark.status.api.v1.InputMetrics
-
- recordsRead() - 类 中的方法org.apache.spark.status.api.v1.ShuffleReadMetrics
-
- recordsWritten() - 类 中的方法org.apache.spark.status.api.v1.OutputMetricDistributions
-
- recordsWritten() - 类 中的方法org.apache.spark.status.api.v1.OutputMetrics
-
- recordsWritten() - 类 中的方法org.apache.spark.status.api.v1.ShuffleWriteMetrics
-
- recordWriter(OutputStream, Configuration) - 类 中的方法org.apache.spark.sql.hive.execution.HiveScriptIOSchema
-
- recordWriterClass() - 类 中的方法org.apache.spark.sql.hive.execution.HiveScriptIOSchema
-
- recoverPartitions(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Recovers all the partitions in the directory of a table and update the catalog.
- RECOVERY_DIRECTORY() - 类 中的静态方法org.apache.spark.internal.config.Deploy
-
- RECOVERY_MODE() - 类 中的静态方法org.apache.spark.internal.config.Deploy
-
- RECOVERY_MODE_FACTORY() - 类 中的静态方法org.apache.spark.internal.config.Deploy
-
- RecursiveFlag - org.apache.spark.ml.image中的类
-
- RecursiveFlag() - 类 的构造器org.apache.spark.ml.image.RecursiveFlag
-
- recursiveList(File) - 类 中的静态方法org.apache.spark.TestUtils
-
Lists files recursively.
- redact(SparkConf, Seq<Tuple2<String, String>>) - 类 中的静态方法org.apache.spark.util.Utils
-
Redact the sensitive values in the given map.
- redact(Option<Regex>, Seq<Tuple2<K, V>>) - 类 中的静态方法org.apache.spark.util.Utils
-
Redact the sensitive values in the given map.
- redact(Option<Regex>, String) - 类 中的静态方法org.apache.spark.util.Utils
-
Redact the sensitive information in the given string.
- redact(Map<String, String>) - 类 中的静态方法org.apache.spark.util.Utils
-
Looks up the redaction regex from within the key value pairs and uses it to redact the rest
of the key value pairs.
- redactCommandLineArgs(SparkConf, Seq<String>) - 类 中的静态方法org.apache.spark.util.Utils
-
- REDIRECT_CONNECTOR_NAME() - 类 中的静态方法org.apache.spark.ui.JettyUtils
-
- redirectableStream() - 类 中的方法org.apache.spark.storage.memory.SerializedValuesHolder
-
- redirectError() - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
Specifies that stderr in spark-submit should be redirected to stdout.
- redirectError(ProcessBuilder.Redirect) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
Redirects error output to the specified Redirect.
- redirectError(File) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
Redirects error output to the specified File.
- redirectOutput(ProcessBuilder.Redirect) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
Redirects standard output to the specified Redirect.
- redirectOutput(File) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
Redirects error output to the specified File.
- redirectToLog(String) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
Sets all output to be logged and redirected to a logger with the specified name.
- reduce(Function2<T, T, T>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Reduces the elements of this RDD using the specified commutative and associative binary
operator.
- reduce(OpenHashMap<String, Object>[], Row) - 类 中的方法org.apache.spark.ml.feature.StringIndexerAggregator
-
- reduce(Function2<T, T, T>) - 类 中的方法org.apache.spark.rdd.RDD
-
Reduces the elements of this RDD using the specified commutative and
associative binary operator.
- reduce(Function2<T, T, T>) - 类 中的方法org.apache.spark.sql.Dataset
-
(Scala-specific)
Reduces the elements of this Dataset using the specified binary function.
- reduce(ReduceFunction<T>) - 类 中的方法org.apache.spark.sql.Dataset
-
(Java-specific)
Reduces the elements of this Dataset using the specified binary function.
- reduce(BUF, IN) - 类 中的方法org.apache.spark.sql.expressions.Aggregator
-
Combine two values to produce a new value.
- reduce(Function2<T, T, T>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD has a single element generated by reducing each RDD
of this DStream.
- reduce(Function2<T, T, T>) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream in which each RDD has a single element generated by reducing each RDD
of this DStream.
- reduceByKey(Partitioner, Function2<V, V, V>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Merge the values for each key using an associative and commutative reduce function.
- reduceByKey(Function2<V, V, V>, int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Merge the values for each key using an associative and commutative reduce function.
- reduceByKey(Function2<V, V, V>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Merge the values for each key using an associative and commutative reduce function.
- reduceByKey(Partitioner, Function2<V, V, V>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Merge the values for each key using an associative and commutative reduce function.
- reduceByKey(Function2<V, V, V>, int) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Merge the values for each key using an associative and commutative reduce function.
- reduceByKey(Function2<V, V, V>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Merge the values for each key using an associative and commutative reduce function.
- reduceByKey(Function2<V, V, V>) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying reduceByKey to each RDD.
- reduceByKey(Function2<V, V, V>, int) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying reduceByKey to each RDD.
- reduceByKey(Function2<V, V, V>, Partitioner) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying reduceByKey to each RDD.
- reduceByKey(Function2<V, V, V>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying reduceByKey to each RDD.
- reduceByKey(Function2<V, V, V>, int) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying reduceByKey to each RDD.
- reduceByKey(Function2<V, V, V>, Partitioner) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying reduceByKey to each RDD.
- reduceByKeyAndWindow(Function2<V, V, V>, Duration) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Create a new DStream by applying reduceByKey over a sliding window on this DStream.
- reduceByKeyAndWindow(Function2<V, V, V>, Duration, Duration) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying reduceByKey over a sliding window.
- reduceByKeyAndWindow(Function2<V, V, V>, Duration, Duration, int) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying reduceByKey over a sliding window.
- reduceByKeyAndWindow(Function2<V, V, V>, Duration, Duration, Partitioner) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying reduceByKey over a sliding window.
- reduceByKeyAndWindow(Function2<V, V, V>, Function2<V, V, V>, Duration, Duration) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by reducing over a using incremental computation.
- reduceByKeyAndWindow(Function2<V, V, V>, Function2<V, V, V>, Duration, Duration, int, Function<Tuple2<K, V>, Boolean>) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying incremental reduceByKey over a sliding window.
- reduceByKeyAndWindow(Function2<V, V, V>, Function2<V, V, V>, Duration, Duration, Partitioner, Function<Tuple2<K, V>, Boolean>) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying incremental reduceByKey over a sliding window.
- reduceByKeyAndWindow(Function2<V, V, V>, Duration) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying reduceByKey over a sliding window on this DStream.
- reduceByKeyAndWindow(Function2<V, V, V>, Duration, Duration) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying reduceByKey over a sliding window.
- reduceByKeyAndWindow(Function2<V, V, V>, Duration, Duration, int) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying reduceByKey over a sliding window.
- reduceByKeyAndWindow(Function2<V, V, V>, Duration, Duration, Partitioner) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying reduceByKey over a sliding window.
- reduceByKeyAndWindow(Function2<V, V, V>, Function2<V, V, V>, Duration, Duration, int, Function1<Tuple2<K, V>, Object>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying incremental reduceByKey over a sliding window.
- reduceByKeyAndWindow(Function2<V, V, V>, Function2<V, V, V>, Duration, Duration, Partitioner, Function1<Tuple2<K, V>, Object>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying incremental reduceByKey over a sliding window.
- reduceByKeyLocally(Function2<V, V, V>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Merge the values for each key using an associative and commutative reduce function, but return
the result immediately to the master as a Map.
- reduceByKeyLocally(Function2<V, V, V>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Merge the values for each key using an associative and commutative reduce function, but return
the results immediately to the master as a Map.
- reduceByWindow(Function2<T, T, T>, Duration, Duration) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD has a single element generated by reducing all
elements in a sliding window over this DStream.
- reduceByWindow(Function2<T, T, T>, Function2<T, T, T>, Duration, Duration) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD has a single element generated by reducing all
elements in a sliding window over this DStream.
- reduceByWindow(Function2<T, T, T>, Duration, Duration) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream in which each RDD has a single element generated by reducing all
elements in a sliding window over this DStream.
- reduceByWindow(Function2<T, T, T>, Function2<T, T, T>, Duration, Duration) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream in which each RDD has a single element generated by reducing all
elements in a sliding window over this DStream.
- ReduceFunction<T> - org.apache.spark.api.java.function中的接口
-
Base interface for function used in Dataset's reduce.
- reduceGroups(Function2<V, V, V>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
(Scala-specific)
Reduces the elements of each group of data using the specified binary function.
- reduceGroups(ReduceFunction<V>) - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
(Java-specific)
Reduces the elements of each group of data using the specified binary function.
- reduceId() - 类 中的方法org.apache.spark.FetchFailed
-
- reduceId() - 类 中的方法org.apache.spark.storage.ShuffleBlockId
-
- reduceId() - 类 中的方法org.apache.spark.storage.ShuffleDataBlockId
-
- reduceId() - 类 中的方法org.apache.spark.storage.ShuffleIndexBlockId
-
- Ref - org.apache.spark.sql.connector.expressions中的类
-
Convenience extractor for any NamedReference.
- Ref() - 类 的构造器org.apache.spark.sql.connector.expressions.Ref
-
- reference(String) - 类 中的静态方法org.apache.spark.sql.connector.expressions.LogicalExpressions
-
- references() - 接口 中的方法org.apache.spark.sql.connector.expressions.Transform
-
Returns all field references in the transform arguments.
- references() - 类 中的方法org.apache.spark.sql.sources.AlwaysFalse
-
- references() - 类 中的方法org.apache.spark.sql.sources.AlwaysTrue
-
- references() - 类 中的方法org.apache.spark.sql.sources.And
-
- references() - 类 中的方法org.apache.spark.sql.sources.EqualNullSafe
-
- references() - 类 中的方法org.apache.spark.sql.sources.EqualTo
-
- references() - 类 中的方法org.apache.spark.sql.sources.Filter
-
List of columns that are referenced by this filter.
- references() - 类 中的方法org.apache.spark.sql.sources.GreaterThan
-
- references() - 类 中的方法org.apache.spark.sql.sources.GreaterThanOrEqual
-
- references() - 类 中的方法org.apache.spark.sql.sources.In
-
- references() - 类 中的方法org.apache.spark.sql.sources.IsNotNull
-
- references() - 类 中的方法org.apache.spark.sql.sources.IsNull
-
- references() - 类 中的方法org.apache.spark.sql.sources.LessThan
-
- references() - 类 中的方法org.apache.spark.sql.sources.LessThanOrEqual
-
- references() - 类 中的方法org.apache.spark.sql.sources.Not
-
- references() - 类 中的方法org.apache.spark.sql.sources.Or
-
- references() - 类 中的方法org.apache.spark.sql.sources.StringContains
-
- references() - 类 中的方法org.apache.spark.sql.sources.StringEndsWith
-
- references() - 类 中的方法org.apache.spark.sql.sources.StringStartsWith
-
- refreshByPath(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Invalidates and refreshes all the cached data (and the associated metadata) for any Dataset
that contains the given data source path.
- refreshTable(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Invalidates and refreshes all the cached data and metadata of the given table.
- regex(Regex) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- regexFromString(String, String) - 类 中的静态方法org.apache.spark.internal.config.ConfigHelpers
-
- regexp_extract(Column, String, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Extract a specific group matched by a Java regex, from the specified string column.
- regexp_replace(Column, String, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Replace all substrings of the specified string value that match regexp with rep.
- regexp_replace(Column, Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Replace all substrings of the specified string value that match regexp with rep.
- RegexTokenizer - org.apache.spark.ml.feature中的类
-
A regex based tokenizer that extracts tokens either by using the provided regex pattern to split
the text (default) or repeatedly matching the regex (if gaps is false).
- RegexTokenizer(String) - 类 的构造器org.apache.spark.ml.feature.RegexTokenizer
-
- RegexTokenizer() - 类 的构造器org.apache.spark.ml.feature.RegexTokenizer
-
- register(SparkContext, Map<String, DoubleAccumulator>) - 类 中的静态方法org.apache.spark.metrics.source.DoubleAccumulatorSource
-
- register(SparkContext, Map<String, LongAccumulator>) - 类 中的静态方法org.apache.spark.metrics.source.LongAccumulatorSource
-
- register(String, RpcEndpoint) - 类 中的方法org.apache.spark.rpc.netty.SharedMessageLoop
-
- register(AccumulatorV2<?, ?>) - 类 中的方法org.apache.spark.SparkContext
-
Register the given accumulator.
- register(AccumulatorV2<?, ?>, String) - 类 中的方法org.apache.spark.SparkContext
-
Register the given accumulator with given name.
- register(String, String) - 类 中的静态方法org.apache.spark.sql.types.UDTRegistration
-
Registers an UserDefinedType to an user class.
- register(String, UserDefinedAggregateFunction) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a user-defined aggregate function (UDAF).
- register(String, UserDefinedFunction) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a user-defined function (UDF), for a UDF that's already defined using the Dataset
API (i.e. of type UserDefinedFunction).
- register(String, Function0<RT>, TypeTags.TypeTag<RT>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 0 arguments as user-defined function (UDF).
- register(String, Function1<A1, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 1 arguments as user-defined function (UDF).
- register(String, Function2<A1, A2, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 2 arguments as user-defined function (UDF).
- register(String, Function3<A1, A2, A3, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 3 arguments as user-defined function (UDF).
- register(String, Function4<A1, A2, A3, A4, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 4 arguments as user-defined function (UDF).
- register(String, Function5<A1, A2, A3, A4, A5, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 5 arguments as user-defined function (UDF).
- register(String, Function6<A1, A2, A3, A4, A5, A6, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 6 arguments as user-defined function (UDF).
- register(String, Function7<A1, A2, A3, A4, A5, A6, A7, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 7 arguments as user-defined function (UDF).
- register(String, Function8<A1, A2, A3, A4, A5, A6, A7, A8, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 8 arguments as user-defined function (UDF).
- register(String, Function9<A1, A2, A3, A4, A5, A6, A7, A8, A9, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 9 arguments as user-defined function (UDF).
- register(String, Function10<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 10 arguments as user-defined function (UDF).
- register(String, Function11<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 11 arguments as user-defined function (UDF).
- register(String, Function12<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 12 arguments as user-defined function (UDF).
- register(String, Function13<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 13 arguments as user-defined function (UDF).
- register(String, Function14<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 14 arguments as user-defined function (UDF).
- register(String, Function15<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>, TypeTags.TypeTag<A15>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 15 arguments as user-defined function (UDF).
- register(String, Function16<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>, TypeTags.TypeTag<A15>, TypeTags.TypeTag<A16>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 16 arguments as user-defined function (UDF).
- register(String, Function17<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>, TypeTags.TypeTag<A15>, TypeTags.TypeTag<A16>, TypeTags.TypeTag<A17>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 17 arguments as user-defined function (UDF).
- register(String, Function18<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>, TypeTags.TypeTag<A15>, TypeTags.TypeTag<A16>, TypeTags.TypeTag<A17>, TypeTags.TypeTag<A18>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 18 arguments as user-defined function (UDF).
- register(String, Function19<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>, TypeTags.TypeTag<A15>, TypeTags.TypeTag<A16>, TypeTags.TypeTag<A17>, TypeTags.TypeTag<A18>, TypeTags.TypeTag<A19>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 19 arguments as user-defined function (UDF).
- register(String, Function20<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>, TypeTags.TypeTag<A15>, TypeTags.TypeTag<A16>, TypeTags.TypeTag<A17>, TypeTags.TypeTag<A18>, TypeTags.TypeTag<A19>, TypeTags.TypeTag<A20>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 20 arguments as user-defined function (UDF).
- register(String, Function21<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, A21, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>, TypeTags.TypeTag<A15>, TypeTags.TypeTag<A16>, TypeTags.TypeTag<A17>, TypeTags.TypeTag<A18>, TypeTags.TypeTag<A19>, TypeTags.TypeTag<A20>, TypeTags.TypeTag<A21>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 21 arguments as user-defined function (UDF).
- register(String, Function22<A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, A21, A22, RT>, TypeTags.TypeTag<RT>, TypeTags.TypeTag<A1>, TypeTags.TypeTag<A2>, TypeTags.TypeTag<A3>, TypeTags.TypeTag<A4>, TypeTags.TypeTag<A5>, TypeTags.TypeTag<A6>, TypeTags.TypeTag<A7>, TypeTags.TypeTag<A8>, TypeTags.TypeTag<A9>, TypeTags.TypeTag<A10>, TypeTags.TypeTag<A11>, TypeTags.TypeTag<A12>, TypeTags.TypeTag<A13>, TypeTags.TypeTag<A14>, TypeTags.TypeTag<A15>, TypeTags.TypeTag<A16>, TypeTags.TypeTag<A17>, TypeTags.TypeTag<A18>, TypeTags.TypeTag<A19>, TypeTags.TypeTag<A20>, TypeTags.TypeTag<A21>, TypeTags.TypeTag<A22>) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Registers a deterministic Scala closure of 22 arguments as user-defined function (UDF).
- register(String, UDF0<?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF0 instance as user-defined function (UDF).
- register(String, UDF1<?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF1 instance as user-defined function (UDF).
- register(String, UDF2<?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF2 instance as user-defined function (UDF).
- register(String, UDF3<?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF3 instance as user-defined function (UDF).
- register(String, UDF4<?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF4 instance as user-defined function (UDF).
- register(String, UDF5<?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF5 instance as user-defined function (UDF).
- register(String, UDF6<?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF6 instance as user-defined function (UDF).
- register(String, UDF7<?, ?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF7 instance as user-defined function (UDF).
- register(String, UDF8<?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF8 instance as user-defined function (UDF).
- register(String, UDF9<?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF9 instance as user-defined function (UDF).
- register(String, UDF10<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF10 instance as user-defined function (UDF).
- register(String, UDF11<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF11 instance as user-defined function (UDF).
- register(String, UDF12<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF12 instance as user-defined function (UDF).
- register(String, UDF13<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF13 instance as user-defined function (UDF).
- register(String, UDF14<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF14 instance as user-defined function (UDF).
- register(String, UDF15<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF15 instance as user-defined function (UDF).
- register(String, UDF16<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF16 instance as user-defined function (UDF).
- register(String, UDF17<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF17 instance as user-defined function (UDF).
- register(String, UDF18<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF18 instance as user-defined function (UDF).
- register(String, UDF19<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF19 instance as user-defined function (UDF).
- register(String, UDF20<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF20 instance as user-defined function (UDF).
- register(String, UDF21<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF21 instance as user-defined function (UDF).
- register(String, UDF22<?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?>, DataType) - 类 中的方法org.apache.spark.sql.UDFRegistration
-
Register a deterministic Java UDF22 instance as user-defined function (UDF).
- register(QueryExecutionListener) - 类 中的方法org.apache.spark.sql.util.ExecutionListenerManager
-
- register(AccumulatorV2<?, ?>) - 类 中的静态方法org.apache.spark.util.AccumulatorContext
-
Registers an
AccumulatorV2 created on the driver such that it can be used on the executors.
- register(String, Function0<Object>) - 类 中的静态方法org.apache.spark.util.SignalUtils
-
Adds an action to be run when a given signal is received by this process.
- registerAvroSchemas(Seq<Schema>) - 类 中的方法org.apache.spark.SparkConf
-
Use Kryo serialization and register the given set of Avro schemas so that the generic
record serializer can decrease network IO
- RegisterBlockManager(BlockManagerId, String[], long, long, org.apache.spark.rpc.RpcEndpointRef) - 类 的构造器org.apache.spark.storage.BlockManagerMessages.RegisterBlockManager
-
- RegisterBlockManager$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.RegisterBlockManager$
-
- registerClasses(Kryo) - 接口 中的方法org.apache.spark.serializer.KryoRegistrator
-
- RegisterClusterManager(org.apache.spark.rpc.RpcEndpointRef) - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterClusterManager
-
- RegisterClusterManager$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterClusterManager$
-
- registerDialect(JdbcDialect) - 类 中的静态方法org.apache.spark.sql.jdbc.JdbcDialects
-
Register a dialect for use on all new matching jdbc org.apache.spark.sql.DataFrame.
- RegisteredExecutor$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisteredExecutor$
-
- RegisterExecutor(String, org.apache.spark.rpc.RpcEndpointRef, String, int, Map<String, String>, Map<String, String>, Map<String, ResourceInformation>) - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterExecutor
-
- RegisterExecutor$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterExecutor$
-
- RegisterExecutorFailed(String) - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterExecutorFailed
-
- RegisterExecutorFailed$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterExecutorFailed$
-
- registerKryoClasses(SparkConf) - 类 中的静态方法org.apache.spark.graphx.GraphXUtils
-
Registers classes that GraphX uses with Kryo.
- registerKryoClasses(SparkContext) - 类 中的静态方法org.apache.spark.ml.evaluation.SquaredEuclideanSilhouette
-
- registerKryoClasses(Class<?>[]) - 类 中的方法org.apache.spark.SparkConf
-
Use Kryo serialization and register the given set of classes with Kryo.
- registerLogger(Logger) - 类 中的静态方法org.apache.spark.util.SignalUtils
-
Register a signal handler to log signals on UNIX-like systems.
- registerShuffle(int) - 接口 中的方法org.apache.spark.shuffle.api.ShuffleDriverComponents
-
Called once per shuffle id when the shuffle id is first generated for a shuffle stage.
- registerShutdownDeleteDir(File) - 类 中的静态方法org.apache.spark.util.ShutdownHookManager
-
- registerStream(DStream<BinarySample>) - 类 中的方法org.apache.spark.mllib.stat.test.StreamingTest
-
Register a DStream of values for significance testing.
- registerStream(JavaDStream<BinarySample>) - 类 中的方法org.apache.spark.mllib.stat.test.StreamingTest
-
Register a JavaDStream of values for significance testing.
- regParam() - 类 中的方法org.apache.spark.ml.classification.LinearSVC
-
- regParam() - 类 中的方法org.apache.spark.ml.classification.LinearSVCModel
-
- regParam() - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
- regParam() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- regParam() - 接口 中的方法org.apache.spark.ml.optim.loss.DifferentiableRegularization
-
Magnitude of the regularization penalty.
- regParam() - 接口 中的方法org.apache.spark.ml.param.shared.HasRegParam
-
Param for regularization parameter (>= 0).
- regParam() - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- regParam() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
- regParam() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
- regParam() - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
- regParam() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionModel
-
- Regression() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.Algo
-
- RegressionEvaluator - org.apache.spark.ml.evaluation中的类
-
Evaluator for regression, which expects two input columns: prediction and label.
- RegressionEvaluator(String) - 类 的构造器org.apache.spark.ml.evaluation.RegressionEvaluator
-
- RegressionEvaluator() - 类 的构造器org.apache.spark.ml.evaluation.RegressionEvaluator
-
- RegressionMetrics - org.apache.spark.mllib.evaluation中的类
-
Evaluator for regression.
- RegressionMetrics(RDD<? extends Product>, boolean) - 类 的构造器org.apache.spark.mllib.evaluation.RegressionMetrics
-
- RegressionMetrics(RDD<? extends Product>) - 类 的构造器org.apache.spark.mllib.evaluation.RegressionMetrics
-
- RegressionModel<FeaturesType,M extends RegressionModel<FeaturesType,M>> - org.apache.spark.ml.regression中的类
-
:: DeveloperApi ::
Model produced by a Regressor.
- RegressionModel() - 类 的构造器org.apache.spark.ml.regression.RegressionModel
-
- RegressionModel - org.apache.spark.mllib.regression中的接口
-
- Regressor<FeaturesType,Learner extends Regressor<FeaturesType,Learner,M>,M extends RegressionModel<FeaturesType,M>> - org.apache.spark.ml.regression中的类
-
Single-label regression
- Regressor() - 类 的构造器org.apache.spark.ml.regression.Regressor
-
- reindex() - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- reindex() - 类 中的方法org.apache.spark.graphx.VertexRDD
-
Construct a new VertexRDD that is indexed by only the visible vertices.
- RelationalGroupedDataset - org.apache.spark.sql中的类
-
A set of methods for aggregations on a
DataFrame, created by
groupBy,
cube or
rollup (and also
pivot).
- RelationalGroupedDataset.CubeType$ - org.apache.spark.sql中的类
-
To indicate it's the CUBE
- RelationalGroupedDataset.GroupByType$ - org.apache.spark.sql中的类
-
To indicate it's the GroupBy
- RelationalGroupedDataset.GroupType - org.apache.spark.sql中的接口
-
The Grouping Type
- RelationalGroupedDataset.PivotType$ - org.apache.spark.sql中的类
-
- RelationalGroupedDataset.RollupType$ - org.apache.spark.sql中的类
-
To indicate it's the ROLLUP
- RelationConversions - org.apache.spark.sql.hive中的类
-
Relation conversion from metastore relations to data source relations for better performance
- When writing to non-partitioned Hive-serde Parquet/Orc tables
- When scanning Hive-serde Parquet/ORC tables
This rule must be run before all other DDL post-hoc resolution rules, i.e.
- RelationConversions(SQLConf, HiveSessionCatalog) - 类 的构造器org.apache.spark.sql.hive.RelationConversions
-
- RelationProvider - org.apache.spark.sql.sources中的接口
-
Implemented by objects that produce relations for a specific kind of data source.
- relativeDirection(long) - 类 中的方法org.apache.spark.graphx.Edge
-
Return the relative direction of the edge to the corresponding
vertex.
- relativeError() - 类 中的方法org.apache.spark.ml.feature.Imputer
-
- relativeError() - 类 中的方法org.apache.spark.ml.feature.ImputerModel
-
- relativeError() - 类 中的方法org.apache.spark.ml.feature.QuantileDiscretizer
-
- relativeError() - 类 中的方法org.apache.spark.ml.feature.RobustScaler
-
- relativeError() - 类 中的方法org.apache.spark.ml.feature.RobustScalerModel
-
- relativeError() - 接口 中的方法org.apache.spark.ml.param.shared.HasRelativeError
-
Param for the relative target precision for the approximate quantile algorithm.
- relativeError() - 类 中的方法org.apache.spark.util.sketch.CountMinSketch
-
- release(Seq<String>) - 接口 中的方法org.apache.spark.resource.ResourceAllocator
-
Release a sequence of resource addresses, these addresses must have been assigned.
- rem(byte, byte) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- rem(Decimal, Decimal) - 类 中的方法org.apache.spark.sql.types.Decimal.DecimalAsIfIntegral$
-
- rem(int, int) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- rem(long, long) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- rem(short, short) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- remainder(Decimal) - 类 中的方法org.apache.spark.sql.types.Decimal
-
- remember(Duration) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Sets each DStreams in this context to remember RDDs it generated in the last given duration.
- remember(Duration) - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Set each DStream in this context to remember RDDs it generated in the last given duration.
- REMOTE_BLOCKS_FETCHED() - 类 中的方法org.apache.spark.InternalAccumulator.shuffleRead$
-
- REMOTE_BYTES_READ() - 类 中的方法org.apache.spark.InternalAccumulator.shuffleRead$
-
- REMOTE_BYTES_READ_TO_DISK() - 类 中的方法org.apache.spark.InternalAccumulator.shuffleRead$
-
- remoteBlocksFetched() - 类 中的方法org.apache.spark.status.api.v1.ShuffleReadMetricDistributions
-
- remoteBlocksFetched() - 类 中的方法org.apache.spark.status.api.v1.ShuffleReadMetrics
-
- remoteBytesRead() - 类 中的方法org.apache.spark.status.api.v1.ShuffleReadMetricDistributions
-
- remoteBytesRead() - 类 中的方法org.apache.spark.status.api.v1.ShuffleReadMetrics
-
- remoteBytesReadToDisk() - 类 中的方法org.apache.spark.status.api.v1.ShuffleReadMetricDistributions
-
- remoteBytesReadToDisk() - 类 中的方法org.apache.spark.status.api.v1.ShuffleReadMetrics
-
- remove(Param<T>) - 类 中的方法org.apache.spark.ml.param.ParamMap
-
Removes a key from this map and returns its value associated previously as an option.
- remove(String) - 类 中的方法org.apache.spark.SparkConf
-
Remove a parameter from the configuration
- remove() - 接口 中的方法org.apache.spark.sql.streaming.GroupState
-
Remove this state.
- remove(String) - 类 中的方法org.apache.spark.sql.types.MetadataBuilder
-
- remove(Object) - 类 中的方法org.apache.spark.sql.util.CaseInsensitiveStringMap
-
- remove() - 类 中的方法org.apache.spark.streaming.State
-
Remove the state if it exists.
- remove(long) - 类 中的静态方法org.apache.spark.util.AccumulatorContext
-
- removeAllListeners() - 接口 中的方法org.apache.spark.util.ListenerBus
-
Remove all listeners and they won't receive any events.
- RemoveBlock(BlockId) - 类 的构造器org.apache.spark.storage.BlockManagerMessages.RemoveBlock
-
- RemoveBlock$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.RemoveBlock$
-
- RemoveBroadcast(long, boolean) - 类 的构造器org.apache.spark.storage.BlockManagerMessages.RemoveBroadcast
-
- RemoveBroadcast$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.RemoveBroadcast$
-
- removeDistribution(LiveExecutor) - 类 中的方法org.apache.spark.status.LiveRDD
-
- RemoveExecutor(String, org.apache.spark.scheduler.ExecutorLossReason) - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RemoveExecutor
-
- RemoveExecutor(String) - 类 的构造器org.apache.spark.storage.BlockManagerMessages.RemoveExecutor
-
- RemoveExecutor$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RemoveExecutor$
-
- RemoveExecutor$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.RemoveExecutor$
-
- removeFromDriver() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.RemoveBroadcast
-
- removeListener(StreamingQueryListener) - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryManager
-
- removeListener(L) - 接口 中的方法org.apache.spark.util.ListenerBus
-
Remove a listener and it won't receive any events.
- removeListenerOnError(SparkListenerInterface) - 类 中的方法org.apache.spark.scheduler.AsyncEventQueue
-
- removeListenerOnError(L) - 接口 中的方法org.apache.spark.util.ListenerBus
-
This can be overridden by subclasses if there is any extra cleanup to do when removing a
listener.
- removeMapOutput(int, BlockManagerId) - 类 中的方法org.apache.spark.ShuffleStatus
-
Remove the map output which was served by the specified block manager.
- removeOutputsByFilter(Function1<BlockManagerId, Object>) - 类 中的方法org.apache.spark.ShuffleStatus
-
Removes all shuffle outputs which satisfies the filter.
- removeOutputsOnExecutor(String) - 类 中的方法org.apache.spark.ShuffleStatus
-
Removes all map outputs associated with the specified executor.
- removeOutputsOnHost(String) - 类 中的方法org.apache.spark.ShuffleStatus
-
Removes all shuffle outputs associated with this host.
- removePartition(String) - 类 中的方法org.apache.spark.status.LiveRDD
-
- removePartition(LiveRDDPartition) - 类 中的方法org.apache.spark.status.RDDPartitionSeq
-
- removeProperty(String) - 接口 中的静态方法org.apache.spark.sql.connector.catalog.NamespaceChange
-
Create a NamespaceChange for removing a namespace property.
- removeProperty(String) - 接口 中的静态方法org.apache.spark.sql.connector.catalog.TableChange
-
Create a TableChange for removing a table property.
- RemoveRdd(int) - 类 的构造器org.apache.spark.storage.BlockManagerMessages.RemoveRdd
-
- RemoveRdd$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.RemoveRdd$
-
- removeReason() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- removeReason() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- removeSchedulable(Schedulable) - 接口 中的方法org.apache.spark.scheduler.Schedulable
-
- removeSelfEdges() - 类 中的方法org.apache.spark.graphx.GraphOps
-
Remove self edges.
- removeShuffle(int, boolean) - 接口 中的方法org.apache.spark.shuffle.api.ShuffleDriverComponents
-
Removes shuffle data associated with the given shuffle.
- RemoveShuffle(int) - 类 的构造器org.apache.spark.storage.BlockManagerMessages.RemoveShuffle
-
- RemoveShuffle$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.RemoveShuffle$
-
- removeShutdownDeleteDir(File) - 类 中的静态方法org.apache.spark.util.ShutdownHookManager
-
- removeShutdownHook(Object) - 类 中的静态方法org.apache.spark.util.ShutdownHookManager
-
Remove a previously installed shutdown hook.
- removeSparkListener(SparkListenerInterface) - 类 中的方法org.apache.spark.SparkContext
-
:: DeveloperApi ::
Deregister the listener from Spark's listener bus.
- removeStreamingListener(StreamingListener) - 类 中的方法org.apache.spark.streaming.StreamingContext
-
- removeTime() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- removeTime() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- RemoveWorker(String, String, String) - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RemoveWorker
-
- RemoveWorker$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RemoveWorker$
-
- renameColumn(String[], String) - 接口 中的静态方法org.apache.spark.sql.connector.catalog.TableChange
-
Create a TableChange for renaming a field.
- renameFunction(String, String, String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Rename an existing function in the database.
- renamePartitions(String, String, Seq<Map<String, String>>, Seq<Map<String, String>>) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Rename one or many existing table partitions, assuming they exist.
- renameTable(Identifier, Identifier) - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- renameTable(Identifier, Identifier) - 接口 中的方法org.apache.spark.sql.connector.catalog.TableCatalog
-
Renames a table in the catalog.
- rep(Function0<Parsers.Parser<T>>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- rep1(Function0<Parsers.Parser<T>>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- rep1(Function0<Parsers.Parser<T>>, Function0<Parsers.Parser<T>>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- rep1sep(Function0<Parsers.Parser<T>>, Function0<Parsers.Parser<Object>>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- repartition(int) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Return a new RDD that has exactly numPartitions partitions.
- repartition(int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return a new RDD that has exactly numPartitions partitions.
- repartition(int) - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Return a new RDD that has exactly numPartitions partitions.
- repartition(int, Ordering<T>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return a new RDD that has exactly numPartitions partitions.
- repartition(int, Column...) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset partitioned by the given partitioning expressions into
numPartitions.
- repartition(Column...) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset partitioned by the given partitioning expressions, using
spark.sql.shuffle.partitions as number of partitions.
- repartition(int) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset that has exactly numPartitions partitions.
- repartition(int, Seq<Column>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset partitioned by the given partitioning expressions into
numPartitions.
- repartition(Seq<Column>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset partitioned by the given partitioning expressions, using
spark.sql.shuffle.partitions as number of partitions.
- repartition(int) - 类 中的方法org.apache.spark.streaming.api.java.JavaDStream
-
Return a new DStream with an increased or decreased level of parallelism.
- repartition(int) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream with an increased or decreased level of parallelism.
- repartition(int) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream with an increased or decreased level of parallelism.
- repartitionAndSortWithinPartitions(Partitioner) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Repartition the RDD according to the given partitioner and, within each resulting partition,
sort records by their keys.
- repartitionAndSortWithinPartitions(Partitioner, Comparator<K>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Repartition the RDD according to the given partitioner and, within each resulting partition,
sort records by their keys.
- repartitionAndSortWithinPartitions(Partitioner) - 类 中的方法org.apache.spark.rdd.OrderedRDDFunctions
-
Repartition the RDD according to the given partitioner and, within each resulting partition,
sort records by their keys.
- repartitionByRange(int, Column...) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset partitioned by the given partitioning expressions into
numPartitions.
- repartitionByRange(Column...) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset partitioned by the given partitioning expressions, using
spark.sql.shuffle.partitions as number of partitions.
- repartitionByRange(int, Seq<Column>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset partitioned by the given partitioning expressions into
numPartitions.
- repartitionByRange(Seq<Column>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset partitioned by the given partitioning expressions, using
spark.sql.shuffle.partitions as number of partitions.
- repeat(Column, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Repeats a string column n times, and returns it as a new string column.
- replace() - 接口 中的方法org.apache.spark.sql.CreateTableWriter
-
Replace an existing table with the contents of the data frame.
- replace(String, Map<T, T>) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Replaces values matching keys in replacement map with the corresponding values.
- replace(String[], Map<T, T>) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
Replaces values matching keys in replacement map with the corresponding values.
- replace(String, Map<T, T>) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
(Scala-specific) Replaces values matching keys in replacement map.
- replace(Seq<String>, Map<T, T>) - 类 中的方法org.apache.spark.sql.DataFrameNaFunctions
-
(Scala-specific) Replaces values matching keys in replacement map.
- replace() - 类 中的方法org.apache.spark.sql.DataFrameWriterV2
-
- replaceCharType(DataType) - 类 中的静态方法org.apache.spark.sql.types.HiveStringType
-
- replicas() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.ReplicateBlock
-
- ReplicateBlock(BlockId, Seq<BlockManagerId>, int) - 类 的构造器org.apache.spark.storage.BlockManagerMessages.ReplicateBlock
-
- ReplicateBlock$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.ReplicateBlock$
-
- replicatedVertexView() - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- replication() - 类 中的方法org.apache.spark.storage.StorageLevel
-
- reply(Object) - 接口 中的方法org.apache.spark.rpc.RpcCallContext
-
Reply a message to the sender.
- repN(int, Function0<Parsers.Parser<T>>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- report() - 接口 中的方法org.apache.spark.metrics.sink.Sink
-
- reportError(String, Throwable) - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Report exceptions in receiving data.
- repsep(Function0<Parsers.Parser<T>>, Function0<Parsers.Parser<Object>>) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- requestedTotal() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RequestExecutors
-
- requesterHost() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.GetLocationsAndStatus
-
- requestExecutors(int) - 接口 中的方法org.apache.spark.ExecutorAllocationClient
-
Request an additional number of executors from the cluster manager.
- RequestExecutors(int, int, Map<String, Object>, Set<String>) - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RequestExecutors
-
- requestExecutors(int) - 类 中的方法org.apache.spark.SparkContext
-
:: DeveloperApi ::
Request an additional number of executors from the cluster manager.
- RequestExecutors$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RequestExecutors$
-
- requestTotalExecutors(int, int, Map<String, Object>) - 接口 中的方法org.apache.spark.ExecutorAllocationClient
-
Update the cluster manager on our scheduling needs.
- requestTotalExecutors(int, int, Map<String, Object>) - 类 中的方法org.apache.spark.SparkContext
-
Update the cluster manager on our scheduling needs.
- res() - 类 中的方法org.apache.spark.mllib.optimization.NNLS.Workspace
-
- reservoirSampleAndCount(Iterator<T>, int, long, ClassTag<T>) - 类 中的静态方法org.apache.spark.util.random.SamplingUtils
-
Reservoir sampling implementation that also returns the input size.
- reset() - 类 中的静态方法org.apache.spark.metrics.source.HiveCatalogMetrics
-
Resets the values of all metrics to zero.
- reset() - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Used for testing only.
- reset() - 类 中的方法org.apache.spark.storage.BufferReleasingInputStream
-
- reset() - 类 中的方法org.apache.spark.util.AccumulatorV2
-
Resets this accumulator, which is zero value. i.e. call isZero must
return true.
- reset() - 类 中的方法org.apache.spark.util.CollectionAccumulator
-
- reset() - 类 中的方法org.apache.spark.util.DoubleAccumulator
-
- reset() - 类 中的方法org.apache.spark.util.LongAccumulator
-
- resetTerminated() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryManager
-
Forget about past terminated queries so that awaitAnyTermination() can be used again to
wait for new terminations.
- residualDegreeOfFreedom() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary
-
- residualDegreeOfFreedomNull() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary
-
- residuals() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary
-
Get the default residuals (deviance residuals) of the fitted model.
- residuals(String) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary
-
Get the residuals of the fitted model by type.
- residuals() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionSummary
-
- ResolveHiveSerdeTable - org.apache.spark.sql.hive中的类
-
Determine the database, serde/format and schema of the Hive serde table, according to the storage
properties.
- ResolveHiveSerdeTable(SparkSession) - 类 的构造器org.apache.spark.sql.hive.ResolveHiveSerdeTable
-
- resolveURI(String) - 类 中的静态方法org.apache.spark.util.Utils
-
Return a well-formed URI for the file described by a user input string.
- resolveURIs(String) - 类 中的静态方法org.apache.spark.util.Utils
-
Resolve a comma-separated list of paths.
- resourceAddresses() - 接口 中的方法org.apache.spark.resource.ResourceAllocator
-
- ResourceAllocator - org.apache.spark.resource中的接口
-
Trait used to help executor/worker allocate resources.
- ResourceInformation - org.apache.spark.resource中的类
-
Class to hold information about a type of Resource.
- ResourceInformation(String, String[]) - 类 的构造器org.apache.spark.resource.ResourceInformation
-
- ResourceInformationJson - org.apache.spark.resource中的类
-
- ResourceInformationJson(String, Seq<String>) - 类 的构造器org.apache.spark.resource.ResourceInformationJson
-
- resourceName() - 接口 中的方法org.apache.spark.resource.ResourceAllocator
-
- resources() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
- resources() - 类 中的方法org.apache.spark.BarrierTaskContext
-
- resources() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterExecutor
-
- resources() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.StatusUpdate
-
- resources() - 类 中的方法org.apache.spark.SparkContext
-
- resources() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- resources() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- resources() - 类 中的方法org.apache.spark.TaskContext
-
Resources allocated to the task.
- resourcesInfo() - 类 中的方法org.apache.spark.scheduler.cluster.ExecutorInfo
-
- resourcesJMap() - 类 中的方法org.apache.spark.BarrierTaskContext
-
- resourcesJMap() - 类 中的方法org.apache.spark.TaskContext
-
(java-specific) Resources allocated to the task.
- resourcesMapFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- resourcesMapToJson(Map<String, ResourceInformation>) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- resourcesMeetRequirements(Map<String, Object>, Seq<ResourceRequirement>) - 类 中的静态方法org.apache.spark.resource.ResourceUtils
-
- ResourceUtils - org.apache.spark.resource中的类
-
- ResourceUtils() - 类 的构造器org.apache.spark.resource.ResourceUtils
-
- responder() - 类 中的方法org.apache.spark.ui.JettyUtils.ServletParams
-
- responseFromBackup(String) - 类 中的静态方法org.apache.spark.util.Utils
-
Return true if the response message is sent from a backup Master on standby.
- restart(String) - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Restart the receiver.
- restart(String, Throwable) - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Restart the receiver.
- restart(String, Throwable, int) - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Restart the receiver.
- ResubmitFailedStages - org.apache.spark.scheduler中的类
-
- ResubmitFailedStages() - 类 的构造器org.apache.spark.scheduler.ResubmitFailedStages
-
- Resubmitted - org.apache.spark中的类
-
:: DeveloperApi ::
A org.apache.spark.scheduler.ShuffleMapTask that completed successfully earlier, but we
lost the executor before the stage completed.
- Resubmitted() - 类 的构造器org.apache.spark.Resubmitted
-
- result(Duration, CanAwait) - 类 中的方法org.apache.spark.ComplexFutureAction
-
- result(Duration, CanAwait) - 接口 中的方法org.apache.spark.FutureAction
-
Awaits and returns the result (of type T) of this action.
- result(Duration, CanAwait) - 类 中的方法org.apache.spark.SimpleFutureAction
-
- RESULT_SERIALIZATION_TIME() - 类 中的静态方法org.apache.spark.InternalAccumulator
-
- RESULT_SERIALIZATION_TIME() - 类 中的静态方法org.apache.spark.ui.jobs.TaskDetailsClassNames
-
- RESULT_SERIALIZATION_TIME() - 类 中的静态方法org.apache.spark.ui.ToolTips
-
- RESULT_SIZE() - 类 中的静态方法org.apache.spark.InternalAccumulator
-
- RESULT_SIZE() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- resultFetchStart() - 类 中的方法org.apache.spark.status.api.v1.TaskData
-
- resultSerializationTime() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- resultSerializationTime() - 类 中的方法org.apache.spark.status.api.v1.TaskMetricDistributions
-
- resultSerializationTime() - 类 中的方法org.apache.spark.status.api.v1.TaskMetrics
-
- resultSetToObjectArray(ResultSet) - 类 中的静态方法org.apache.spark.rdd.JdbcRDD
-
- resultSize() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- resultSize() - 类 中的方法org.apache.spark.status.api.v1.TaskMetricDistributions
-
- resultSize() - 类 中的方法org.apache.spark.status.api.v1.TaskMetrics
-
- RETAINED_APPLICATIONS() - 类 中的静态方法org.apache.spark.internal.config.Deploy
-
- RETAINED_APPLICATIONS() - 类 中的静态方法org.apache.spark.internal.config.History
-
- RETAINED_DRIVERS() - 类 中的静态方法org.apache.spark.internal.config.Deploy
-
- RetrieveDelegationTokens$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RetrieveDelegationTokens$
-
- RetrieveLastAllocatedExecutorId$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RetrieveLastAllocatedExecutorId$
-
- RetrieveSparkAppConfig$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RetrieveSparkAppConfig$
-
- retryWaitMs(SparkConf) - 类 中的静态方法org.apache.spark.util.RpcUtils
-
Returns the configured number of milliseconds to wait on each retry
- ReturnStatementFinder - org.apache.spark.util中的类
-
- ReturnStatementFinder(Option<String>) - 类 的构造器org.apache.spark.util.ReturnStatementFinder
-
- reverse() - 类 中的方法org.apache.spark.graphx.EdgeDirection
-
Reverse the direction of an edge.
- reverse() - 类 中的方法org.apache.spark.graphx.EdgeRDD
-
Reverse all the edges in this RDD.
- reverse() - 类 中的方法org.apache.spark.graphx.Graph
-
Reverses all edges in the graph.
- reverse() - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
- reverse() - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- reverse(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns a reversed string or an array with reverse order of elements.
- reverse() - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- reverse() - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- reverse() - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- reverse() - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- reverse() - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- reverse() - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- reverse() - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- reversed() - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- reversed() - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- reversed() - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- reversed() - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- reversed() - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- reversed() - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- reversed() - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- reverseRoutingTables() - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- reverseRoutingTables() - 类 中的方法org.apache.spark.graphx.VertexRDD
-
Returns a new
VertexRDD reflecting a reversal of all edge directions in the corresponding
EdgeRDD.
- ReviveOffers - org.apache.spark.scheduler.local中的类
-
- ReviveOffers() - 类 的构造器org.apache.spark.scheduler.local.ReviveOffers
-
- reviveOffers() - 接口 中的方法org.apache.spark.scheduler.SchedulerBackend
-
- ReviveOffers$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.ReviveOffers$
-
- RewritableTransform - org.apache.spark.sql.connector.expressions中的接口
-
Allows Spark to rewrite the given references of the transform during analysis.
- RFormula - org.apache.spark.ml.feature中的类
-
Implements the transforms required for fitting a dataset against an R model formula.
- RFormula(String) - 类 的构造器org.apache.spark.ml.feature.RFormula
-
- RFormula() - 类 的构造器org.apache.spark.ml.feature.RFormula
-
- RFormulaBase - org.apache.spark.ml.feature中的接口
-
- RFormulaModel - org.apache.spark.ml.feature中的类
-
- RFormulaParser - org.apache.spark.ml.feature中的类
-
Limited implementation of R formula parsing.
- RFormulaParser() - 类 的构造器org.apache.spark.ml.feature.RFormulaParser
-
- RidgeRegressionModel - org.apache.spark.mllib.regression中的类
-
Regression model trained using RidgeRegression.
- RidgeRegressionModel(Vector, double) - 类 的构造器org.apache.spark.mllib.regression.RidgeRegressionModel
-
- RidgeRegressionWithSGD - org.apache.spark.mllib.regression中的类
-
Train a regression model with L2-regularization using Stochastic Gradient Descent.
- right() - 类 中的方法org.apache.spark.sql.sources.And
-
- right() - 类 中的方法org.apache.spark.sql.sources.Or
-
- rightCategories() - 类 中的方法org.apache.spark.ml.tree.CategoricalSplit
-
Get sorted categories which split to the right
- rightChild() - 类 中的方法org.apache.spark.ml.tree.DecisionTreeModelReadWrite.NodeData
-
- rightChild() - 类 中的方法org.apache.spark.ml.tree.InternalNode
-
- rightChildIndex(int) - 类 中的静态方法org.apache.spark.mllib.tree.model.Node
-
Return the index of the right child of this node.
- rightImpurity() - 类 中的方法org.apache.spark.mllib.tree.model.InformationGainStats
-
- rightNode() - 类 中的方法org.apache.spark.mllib.tree.model.Node
-
- rightNodeId() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.NodeData
-
- rightOuterJoin(JavaPairRDD<K, W>, Partitioner) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Perform a right outer join of this and other.
- rightOuterJoin(JavaPairRDD<K, W>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Perform a right outer join of this and other.
- rightOuterJoin(JavaPairRDD<K, W>, int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Perform a right outer join of this and other.
- rightOuterJoin(RDD<Tuple2<K, W>>, Partitioner) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Perform a right outer join of this and other.
- rightOuterJoin(RDD<Tuple2<K, W>>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Perform a right outer join of this and other.
- rightOuterJoin(RDD<Tuple2<K, W>>, int) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Perform a right outer join of this and other.
- rightOuterJoin(JavaPairDStream<K, W>) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying 'right outer join' between RDDs of this DStream and
other DStream.
- rightOuterJoin(JavaPairDStream<K, W>, int) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying 'right outer join' between RDDs of this DStream and
other DStream.
- rightOuterJoin(JavaPairDStream<K, W>, Partitioner) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Return a new DStream by applying 'right outer join' between RDDs of this DStream and
other DStream.
- rightOuterJoin(DStream<Tuple2<K, W>>, ClassTag<W>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying 'right outer join' between RDDs of this DStream and
other DStream.
- rightOuterJoin(DStream<Tuple2<K, W>>, int, ClassTag<W>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying 'right outer join' between RDDs of this DStream and
other DStream.
- rightOuterJoin(DStream<Tuple2<K, W>>, Partitioner, ClassTag<W>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Return a new DStream by applying 'right outer join' between RDDs of this DStream and
other DStream.
- rightPredict() - 类 中的方法org.apache.spark.mllib.tree.model.InformationGainStats
-
- rint(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the double value that is closest in value to the argument and
is equal to a mathematical integer.
- rint(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the double value that is closest in value to the argument and
is equal to a mathematical integer.
- rlike(String) - 类 中的方法org.apache.spark.sql.Column
-
SQL RLIKE expression (LIKE with Regex).
- RMATa() - 类 中的静态方法org.apache.spark.graphx.util.GraphGenerators
-
- RMATb() - 类 中的静态方法org.apache.spark.graphx.util.GraphGenerators
-
- RMATc() - 类 中的静态方法org.apache.spark.graphx.util.GraphGenerators
-
- RMATd() - 类 中的静态方法org.apache.spark.graphx.util.GraphGenerators
-
- rmatGraph(SparkContext, int, int) - 类 中的静态方法org.apache.spark.graphx.util.GraphGenerators
-
A random graph generator using the R-MAT model, proposed in
"R-MAT: A Recursive Model for Graph Mining" by Chakrabarti et al.
- rnd() - 类 中的方法org.apache.spark.rdd.DefaultPartitionCoalescer
-
- RobustScaler - org.apache.spark.ml.feature中的类
-
Scale features using statistics that are robust to outliers.
- RobustScaler(String) - 类 的构造器org.apache.spark.ml.feature.RobustScaler
-
- RobustScaler() - 类 的构造器org.apache.spark.ml.feature.RobustScaler
-
- RobustScalerModel - org.apache.spark.ml.feature中的类
-
- RobustScalerParams - org.apache.spark.ml.feature中的接口
-
- roc() - 接口 中的方法org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
-
Returns the receiver operating characteristic (ROC) curve,
which is a Dataframe having two fields (FPR, TPR)
with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
- roc() - 类 中的方法org.apache.spark.ml.classification.BinaryLogisticRegressionSummaryImpl
-
- roc() - 类 中的方法org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
-
Returns the receiver operating characteristic (ROC) curve,
which is an RDD of (false positive rate, true positive rate)
with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
- rolledOver() - 接口 中的方法org.apache.spark.util.logging.RollingPolicy
-
Notify that rollover has occurred
- RollingPolicy - org.apache.spark.util.logging中的接口
-
Defines the policy based on which RollingFileAppender will
generate rolling files.
- rollup(Column...) - 类 中的方法org.apache.spark.sql.Dataset
-
Create a multi-dimensional rollup for the current Dataset using the specified columns,
so we can run aggregation on them.
- rollup(String, String...) - 类 中的方法org.apache.spark.sql.Dataset
-
Create a multi-dimensional rollup for the current Dataset using the specified columns,
so we can run aggregation on them.
- rollup(Seq<Column>) - 类 中的方法org.apache.spark.sql.Dataset
-
Create a multi-dimensional rollup for the current Dataset using the specified columns,
so we can run aggregation on them.
- rollup(String, Seq<String>) - 类 中的方法org.apache.spark.sql.Dataset
-
Create a multi-dimensional rollup for the current Dataset using the specified columns,
so we can run aggregation on them.
- RollupType$() - 类 的构造器org.apache.spark.sql.RelationalGroupedDataset.RollupType$
-
- rootAllocator() - 类 中的静态方法org.apache.spark.sql.util.ArrowUtils
-
- rootMeanSquaredError() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionSummary
-
Returns the root mean squared error, which is defined as the square root of
the mean squared error.
- rootMeanSquaredError() - 类 中的方法org.apache.spark.mllib.evaluation.RegressionMetrics
-
Returns the root mean squared error, which is defined as the square root of
the mean squared error.
- rootNode() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- rootNode() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- rootNode() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeModel
-
Root of the decision tree
- rootPool() - 接口 中的方法org.apache.spark.scheduler.SchedulableBuilder
-
- rootPool() - 接口 中的方法org.apache.spark.scheduler.TaskScheduler
-
- round(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the value of the column e rounded to 0 decimal places with HALF_UP round mode.
- round(Column, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Round the value of e to scale decimal places with HALF_UP round mode
if scale is greater than or equal to 0 or at integral part when scale is less than 0.
- ROUND_CEILING() - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- ROUND_FLOOR() - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- ROUND_HALF_EVEN() - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- ROUND_HALF_UP() - 类 中的静态方法org.apache.spark.sql.types.Decimal
-
- ROW() - 类 中的静态方法org.apache.spark.api.r.SerializationFormats
-
- Row - org.apache.spark.sql中的接口
-
Represents one row of output from a relational operator.
- row(T) - 接口 中的方法org.apache.spark.ui.PagedTable
-
- row_number() - 类 中的静态方法org.apache.spark.sql.functions
-
Window function: returns a sequential number starting at 1 within a window partition.
- RowFactory - org.apache.spark.sql中的类
-
A factory class used to construct
Row objects.
- RowFactory() - 类 的构造器org.apache.spark.sql.RowFactory
-
- rowIndices() - 类 中的方法org.apache.spark.ml.linalg.SparseMatrix
-
- rowIndices() - 类 中的方法org.apache.spark.mllib.linalg.SparseMatrix
-
- rowIter() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Returns an iterator of row vectors.
- rowIter() - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
Returns an iterator of row vectors.
- rowIterator() - 类 中的方法org.apache.spark.sql.vectorized.ColumnarBatch
-
Returns an iterator over the rows in this batch.
- RowMatrix - org.apache.spark.mllib.linalg.distributed中的类
-
Represents a row-oriented distributed Matrix with no meaningful row indices.
- RowMatrix(RDD<Vector>, long, int) - 类 的构造器org.apache.spark.mllib.linalg.distributed.RowMatrix
-
- RowMatrix(RDD<Vector>) - 类 的构造器org.apache.spark.mllib.linalg.distributed.RowMatrix
-
Alternative constructor leaving matrix dimensions to be determined automatically.
- rows() - 类 中的方法org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
-
- rows() - 类 中的方法org.apache.spark.mllib.linalg.distributed.RowMatrix
-
- rowsBetween(long, long) - 类 中的静态方法org.apache.spark.sql.expressions.Window
-
Creates a
WindowSpec with the frame boundaries defined,
from
start (inclusive) to
end (inclusive).
- rowsBetween(long, long) - 类 中的方法org.apache.spark.sql.expressions.WindowSpec
-
Defines the frame boundaries, from start (inclusive) to end (inclusive).
- rowsPerBlock() - 类 中的方法org.apache.spark.mllib.linalg.distributed.BlockMatrix
-
- rPackages() - 类 中的静态方法org.apache.spark.api.r.RUtils
-
- rpad(Column, int, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Right-pad the string column with pad to a length of len.
- RpcAbortException - org.apache.spark.rpc中的异常错误
-
An exception thrown if the RPC is aborted.
- RpcAbortException(String) - 异常错误 的构造器org.apache.spark.rpc.RpcAbortException
-
- RpcCallContext - org.apache.spark.rpc中的接口
-
A callback that
RpcEndpoint can use to send back a message or failure.
- RpcEndpoint - org.apache.spark.rpc中的接口
-
An end point for the RPC that defines what functions to trigger given a message.
- rpcEnv() - 接口 中的方法org.apache.spark.rpc.RpcEndpoint
-
- RpcEnvFactory - org.apache.spark.rpc中的接口
-
A factory class to create the RpcEnv.
- RpcEnvFileServer - org.apache.spark.rpc中的接口
-
A server used by the RpcEnv to server files to other processes owned by the application.
- RpcUtils - org.apache.spark.util中的类
-
- RpcUtils() - 类 的构造器org.apache.spark.util.RpcUtils
-
- RRDD<T> - org.apache.spark.api.r中的类
-
An RDD that stores serialized R objects as Array[Byte].
- RRDD(RDD<T>, byte[], String, String, byte[], Object[], ClassTag<T>) - 类 的构造器org.apache.spark.api.r.RRDD
-
- RRunnerModes - org.apache.spark.api.r中的类
-
- RRunnerModes() - 类 的构造器org.apache.spark.api.r.RRunnerModes
-
- rtrim(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Trim the spaces from right end for the specified string value.
- rtrim(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Trim the specified character string from right end for the specified string column.
- ruleName() - 类 中的静态方法org.apache.spark.sql.dynamicpruning.CleanupDynamicPruningFilters
-
- ruleName() - 类 中的静态方法org.apache.spark.sql.dynamicpruning.PartitionPruning
-
- ruleName() - 类 中的静态方法org.apache.spark.sql.hive.HiveAnalysis
-
- run(Graph<VD, ED>, int, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.lib.ConnectedComponents
-
Compute the connected component membership of each vertex and return a graph with the vertex
value containing the lowest vertex id in the connected component containing that vertex.
- run(Graph<VD, ED>, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.lib.ConnectedComponents
-
Compute the connected component membership of each vertex and return a graph with the vertex
value containing the lowest vertex id in the connected component containing that vertex.
- run(Graph<VD, ED>, int, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.lib.LabelPropagation
-
Run static Label Propagation for detecting communities in networks.
- run(Graph<VD, ED>, int, double, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.lib.PageRank
-
Run PageRank for a fixed number of iterations returning a graph
with vertex attributes containing the PageRank and edge
attributes the normalized edge weight.
- run(Graph<VD, ED>, Seq<Object>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.lib.ShortestPaths
-
Computes shortest paths to the given set of landmark vertices.
- run(Graph<VD, ED>, int, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.lib.StronglyConnectedComponents
-
Compute the strongly connected component (SCC) of each vertex and return a graph with the
vertex value containing the lowest vertex id in the SCC containing that vertex.
- run(RDD<Edge<Object>>, SVDPlusPlus.Conf) - 类 中的静态方法org.apache.spark.graphx.lib.SVDPlusPlus
-
Implement SVD++ based on "Factorization Meets the Neighborhood:
a Multifaceted Collaborative Filtering Model",
available at
here.
- run(Graph<VD, ED>, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.lib.TriangleCount
-
- run(RDD<org.apache.spark.ml.feature.Instance>, BoostingStrategy, long, String) - 类 中的静态方法org.apache.spark.ml.tree.impl.GradientBoostedTrees
-
Method to train a gradient boosting model
- run(RDD<LabeledPoint>, Strategy, int, String, long) - 类 中的静态方法org.apache.spark.ml.tree.impl.RandomForest
-
Train a random forest.
- run(RDD<org.apache.spark.ml.feature.Instance>, Strategy, int, String, long, Option<org.apache.spark.ml.util.Instrumentation>, boolean, Option<String>) - 类 中的静态方法org.apache.spark.ml.tree.impl.RandomForest
-
Train a random forest.
- run(RDD<LabeledPoint>) - 类 中的方法org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
-
Run Logistic Regression with the configured parameters on an input RDD
of LabeledPoint entries.
- run(RDD<LabeledPoint>, Vector) - 类 中的方法org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
-
Run Logistic Regression with the configured parameters on an input RDD
of LabeledPoint entries starting from the initial weights provided.
- run(RDD<LabeledPoint>) - 类 中的方法org.apache.spark.mllib.classification.NaiveBayes
-
Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.
- run(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeans
-
Runs the bisecting k-means algorithm.
- run(JavaRDD<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeans
-
Java-friendly version of run().
- run(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixture
-
Perform expectation maximization
- run(JavaRDD<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixture
-
Java-friendly version of run()
- run(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.KMeans
-
Train a K-means model on the given set of points; data should be cached for high
performance, because this is an iterative algorithm.
- run(RDD<Tuple2<Object, Vector>>) - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Learn an LDA model using the given dataset.
- run(JavaPairRDD<Long, Vector>) - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Java-friendly version of run()
- run(Graph<Object, Object>) - 类 中的方法org.apache.spark.mllib.clustering.PowerIterationClustering
-
Run the PIC algorithm on Graph.
- run(RDD<Tuple3<Object, Object, Object>>) - 类 中的方法org.apache.spark.mllib.clustering.PowerIterationClustering
-
Run the PIC algorithm.
- run(JavaRDD<Tuple3<Long, Long, Double>>) - 类 中的方法org.apache.spark.mllib.clustering.PowerIterationClustering
-
A Java-friendly version of PowerIterationClustering.run.
- run(RDD<FPGrowth.FreqItemset<Item>>, ClassTag<Item>) - 类 中的方法org.apache.spark.mllib.fpm.AssociationRules
-
Computes the association rules with confidence above minConfidence.
- run(RDD<FPGrowth.FreqItemset<Item>>, Map<Item, Object>, ClassTag<Item>) - 类 中的方法org.apache.spark.mllib.fpm.AssociationRules
-
Computes the association rules with confidence above minConfidence.
- run(JavaRDD<FPGrowth.FreqItemset<Item>>) - 类 中的方法org.apache.spark.mllib.fpm.AssociationRules
-
Java-friendly version of run.
- run(RDD<Object>, ClassTag<Item>) - 类 中的方法org.apache.spark.mllib.fpm.FPGrowth
-
Computes an FP-Growth model that contains frequent itemsets.
- run(JavaRDD<Basket>) - 类 中的方法org.apache.spark.mllib.fpm.FPGrowth
-
Java-friendly version of run.
- run(RDD<Object[]>, ClassTag<Item>) - 类 中的方法org.apache.spark.mllib.fpm.PrefixSpan
-
Finds the complete set of frequent sequential patterns in the input sequences of itemsets.
- run(JavaRDD<Sequence>) - 类 中的方法org.apache.spark.mllib.fpm.PrefixSpan
-
A Java-friendly version of
run() that reads sequences from a
JavaRDD and returns
frequent sequences in a
PrefixSpanModel.
- run(RDD<Rating>) - 类 中的方法org.apache.spark.mllib.recommendation.ALS
-
Run ALS with the configured parameters on an input RDD of
Rating objects.
- run(JavaRDD<Rating>) - 类 中的方法org.apache.spark.mllib.recommendation.ALS
-
Java-friendly version of ALS.run.
- run(RDD<LabeledPoint>) - 类 中的方法org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
-
Run the algorithm with the configured parameters on an input
RDD of LabeledPoint entries.
- run(RDD<LabeledPoint>, Vector) - 类 中的方法org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
-
Run the algorithm with the configured parameters on an input RDD
of LabeledPoint entries starting from the initial weights provided.
- run(RDD<Tuple3<Object, Object, Object>>) - 类 中的方法org.apache.spark.mllib.regression.IsotonicRegression
-
Run IsotonicRegression algorithm to obtain isotonic regression model.
- run(JavaRDD<Tuple3<Double, Double, Double>>) - 类 中的方法org.apache.spark.mllib.regression.IsotonicRegression
-
Run pool adjacent violators algorithm to obtain isotonic regression model.
- run(RDD<LabeledPoint>) - 类 中的方法org.apache.spark.mllib.tree.DecisionTree
-
Method to train a decision tree model over an RDD
- run(RDD<LabeledPoint>) - 类 中的方法org.apache.spark.mllib.tree.GradientBoostedTrees
-
Method to train a gradient boosting model
- run(JavaRDD<LabeledPoint>) - 类 中的方法org.apache.spark.mllib.tree.GradientBoostedTrees
-
Java-friendly API for org.apache.spark.mllib.tree.GradientBoostedTrees.run.
- run(RDD<LabeledPoint>) - 类 中的方法org.apache.spark.mllib.tree.RandomForest
-
Method to train a decision tree model over an RDD
- run(SparkSession, SparkPlan) - 接口 中的方法org.apache.spark.sql.hive.execution.CreateHiveTableAsSelectBase
-
- run(SparkSession, SparkPlan) - 类 中的方法org.apache.spark.sql.hive.execution.InsertIntoHiveDirCommand
-
- run(SparkSession, SparkPlan) - 类 中的方法org.apache.spark.sql.hive.execution.InsertIntoHiveTable
-
Inserts all the rows in the table into Hive.
- run() - 类 中的方法org.apache.spark.sql.hive.execution.ScriptTransformationWriterThread
-
- run() - 类 中的方法org.apache.spark.util.SparkShutdownHook
-
- runApproximateJob(RDD<T>, Function2<TaskContext, Iterator<T>, U>, ApproximateEvaluator<U, R>, long) - 类 中的方法org.apache.spark.SparkContext
-
:: DeveloperApi ::
Run a job that can return approximate results.
- runId() - 接口 中的方法org.apache.spark.sql.streaming.StreamingQuery
-
Returns the unique id of this run of the query.
- runId() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryListener.QueryStartedEvent
-
- runId() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryListener.QueryTerminatedEvent
-
- runId() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryProgress
-
- runInNewThread(String, boolean, Function0<T>) - 类 中的静态方法org.apache.spark.util.ThreadUtils
-
Run a piece of code in a new thread and return the result.
- runJob(RDD<T>, Function2<TaskContext, Iterator<T>, U>, Seq<Object>, Function2<Object, U, BoxedUnit>, ClassTag<U>) - 类 中的方法org.apache.spark.SparkContext
-
Run a function on a given set of partitions in an RDD and pass the results to the given
handler function.
- runJob(RDD<T>, Function2<TaskContext, Iterator<T>, U>, Seq<Object>, ClassTag<U>) - 类 中的方法org.apache.spark.SparkContext
-
Run a function on a given set of partitions in an RDD and return the results as an array.
- runJob(RDD<T>, Function1<Iterator<T>, U>, Seq<Object>, ClassTag<U>) - 类 中的方法org.apache.spark.SparkContext
-
Run a function on a given set of partitions in an RDD and return the results as an array.
- runJob(RDD<T>, Function2<TaskContext, Iterator<T>, U>, ClassTag<U>) - 类 中的方法org.apache.spark.SparkContext
-
Run a job on all partitions in an RDD and return the results in an array.
- runJob(RDD<T>, Function1<Iterator<T>, U>, ClassTag<U>) - 类 中的方法org.apache.spark.SparkContext
-
Run a job on all partitions in an RDD and return the results in an array.
- runJob(RDD<T>, Function2<TaskContext, Iterator<T>, U>, Function2<Object, U, BoxedUnit>, ClassTag<U>) - 类 中的方法org.apache.spark.SparkContext
-
Run a job on all partitions in an RDD and pass the results to a handler function.
- runJob(RDD<T>, Function1<Iterator<T>, U>, Function2<Object, U, BoxedUnit>, ClassTag<U>) - 类 中的方法org.apache.spark.SparkContext
-
Run a job on all partitions in an RDD and pass the results to a handler function.
- runLBFGS(RDD<Tuple2<Object, Vector>>, Gradient, Updater, int, double, int, double, Vector) - 类 中的静态方法org.apache.spark.mllib.optimization.LBFGS
-
Run Limited-memory BFGS (L-BFGS) in parallel.
- runMiniBatchSGD(RDD<Tuple2<Object, Vector>>, Gradient, Updater, double, int, double, double, Vector, double) - 类 中的静态方法org.apache.spark.mllib.optimization.GradientDescent
-
Run stochastic gradient descent (SGD) in parallel using mini batches.
- runMiniBatchSGD(RDD<Tuple2<Object, Vector>>, Gradient, Updater, double, int, double, double, Vector) - 类 中的静态方法org.apache.spark.mllib.optimization.GradientDescent
-
Alias of runMiniBatchSGD with convergenceTol set to default value of 0.001.
- running() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
- RUNNING() - 类 中的静态方法org.apache.spark.TaskState
-
- runningTasks() - 接口 中的方法org.apache.spark.scheduler.Schedulable
-
- runParallelPersonalizedPageRank(Graph<VD, ED>, int, double, long[], ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.lib.PageRank
-
Run Personalized PageRank for a fixed number of iterations, for a
set of starting nodes in parallel.
- runPreCanonicalized(Graph<VD, ED>, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.lib.TriangleCount
-
- runSqlHive(String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Runs a HiveQL command using Hive, returning the results as a list of strings.
- runtime() - 类 中的方法org.apache.spark.status.api.v1.ApplicationEnvironmentInfo
-
- RuntimeConfig - org.apache.spark.sql中的类
-
Runtime configuration interface for Spark.
- RuntimeInfo - org.apache.spark.status.api.v1中的类
-
- RuntimePercentage - org.apache.spark.scheduler中的类
-
- RuntimePercentage(double, Option<Object>, double) - 类 的构造器org.apache.spark.scheduler.RuntimePercentage
-
- runUntilConvergence(Graph<VD, ED>, double, double, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.lib.PageRank
-
Run a dynamic version of PageRank returning a graph with vertex attributes containing the
PageRank and edge attributes containing the normalized edge weight.
- runUntilConvergenceWithOptions(Graph<VD, ED>, double, double, Option<Object>, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.lib.PageRank
-
Run a dynamic version of PageRank returning a graph with vertex attributes containing the
PageRank and edge attributes containing the normalized edge weight.
- runWithOptions(Graph<VD, ED>, int, double, Option<Object>, ClassTag<VD>, ClassTag<ED>) - 类 中的静态方法org.apache.spark.graphx.lib.PageRank
-
Run PageRank for a fixed number of iterations returning a graph
with vertex attributes containing the PageRank and edge
attributes the normalized edge weight.
- runWithValidation(RDD<org.apache.spark.ml.feature.Instance>, RDD<org.apache.spark.ml.feature.Instance>, BoostingStrategy, long, String) - 类 中的静态方法org.apache.spark.ml.tree.impl.GradientBoostedTrees
-
Method to validate a gradient boosting model
- runWithValidation(RDD<LabeledPoint>, RDD<LabeledPoint>) - 类 中的方法org.apache.spark.mllib.tree.GradientBoostedTrees
-
Method to validate a gradient boosting model
- runWithValidation(JavaRDD<LabeledPoint>, JavaRDD<LabeledPoint>) - 类 中的方法org.apache.spark.mllib.tree.GradientBoostedTrees
-
Java-friendly API for org.apache.spark.mllib.tree.GradientBoostedTrees.runWithValidation.
- RUtils - org.apache.spark.api.r中的类
-
- RUtils() - 类 的构造器org.apache.spark.api.r.RUtils
-
- RWrappers - org.apache.spark.ml.r中的类
-
This is the Scala stub of SparkR read.ml.
- RWrappers() - 类 的构造器org.apache.spark.ml.r.RWrappers
-
- RWrapperUtils - org.apache.spark.ml.r中的类
-
- RWrapperUtils() - 类 的构造器org.apache.spark.ml.r.RWrapperUtils
-
- s() - 类 中的方法org.apache.spark.mllib.linalg.SingularValueDecomposition
-
- safeCall(Function0<T>) - 接口 中的方法org.apache.spark.security.CryptoStreamUtils.BaseErrorHandler
-
- SAFEMODE_CHECK_INTERVAL_S() - 类 中的静态方法org.apache.spark.internal.config.History
-
- sameThread() - 类 中的静态方法org.apache.spark.util.ThreadUtils
-
An ExecutionContextExecutor that runs each task in the thread that invokes execute/submit.
- sample(boolean, Double) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Return a sampled subset of this RDD.
- sample(boolean, Double, long) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Return a sampled subset of this RDD.
- sample(boolean, double) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return a sampled subset of this RDD.
- sample(boolean, double, long) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return a sampled subset of this RDD.
- sample(boolean, double) - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Return a sampled subset of this RDD with a random seed.
- sample(boolean, double, long) - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Return a sampled subset of this RDD, with a user-supplied seed.
- sample(boolean, double, long) - 类 中的方法org.apache.spark.rdd.RDD
-
Return a sampled subset of this RDD.
- sample(double, long) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new
Dataset by sampling a fraction of rows (without replacement),
using a user-supplied seed.
- sample(double) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new
Dataset by sampling a fraction of rows (without replacement),
using a random seed.
- sample(boolean, double, long) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new
Dataset by sampling a fraction of rows, using a user-supplied seed.
- sample(boolean, double) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new
Dataset by sampling a fraction of rows, using a random seed.
- sample() - 类 中的方法org.apache.spark.util.random.BernoulliCellSampler
-
- sample() - 类 中的方法org.apache.spark.util.random.BernoulliSampler
-
- sample() - 类 中的方法org.apache.spark.util.random.PoissonSampler
-
- sample(Iterator<T>) - 类 中的方法org.apache.spark.util.random.PoissonSampler
-
- sample(Iterator<T>) - 接口 中的方法org.apache.spark.util.random.RandomSampler
-
take a random sample
- sample() - 接口 中的方法org.apache.spark.util.random.RandomSampler
-
Whether to sample the next item or not.
- sampleBy(String, Map<T, Object>, long) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
Returns a stratified sample without replacement based on the fraction given on each stratum.
- sampleBy(String, Map<T, Double>, long) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
Returns a stratified sample without replacement based on the fraction given on each stratum.
- sampleBy(Column, Map<T, Object>, long) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
Returns a stratified sample without replacement based on the fraction given on each stratum.
- sampleBy(Column, Map<T, Double>, long) - 类 中的方法org.apache.spark.sql.DataFrameStatFunctions
-
(Java-specific) Returns a stratified sample without replacement based on the fraction given
on each stratum.
- sampleByKey(boolean, Map<K, Double>, long) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return a subset of this RDD sampled by key (via stratified sampling).
- sampleByKey(boolean, Map<K, Double>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return a subset of this RDD sampled by key (via stratified sampling).
- sampleByKey(boolean, Map<K, Object>, long) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Return a subset of this RDD sampled by key (via stratified sampling).
- sampleByKeyExact(boolean, Map<K, Double>, long) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return a subset of this RDD sampled by key (via stratified sampling) containing exactly
math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
- sampleByKeyExact(boolean, Map<K, Double>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return a subset of this RDD sampled by key (via stratified sampling) containing exactly
math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
- sampleByKeyExact(boolean, Map<K, Object>, long) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Return a subset of this RDD sampled by key (via stratified sampling) containing exactly
math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
- SamplePathFilter - org.apache.spark.ml.image中的类
-
Filter that allows loading a fraction of HDFS files.
- SamplePathFilter() - 类 的构造器org.apache.spark.ml.image.SamplePathFilter
-
- samplePointsPerPartitionHint() - 类 中的方法org.apache.spark.RangePartitioner
-
- sampleRatio() - 类 中的方法org.apache.spark.ml.image.SamplePathFilter
-
- sampleStdev() - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Compute the sample standard deviation of this RDD's elements (which corrects for bias in
estimating the standard deviation by dividing by N-1 instead of N).
- sampleStdev() - 类 中的方法org.apache.spark.rdd.DoubleRDDFunctions
-
Compute the sample standard deviation of this RDD's elements (which corrects for bias in
estimating the standard deviation by dividing by N-1 instead of N).
- sampleStdev() - 类 中的方法org.apache.spark.util.StatCounter
-
Return the sample standard deviation of the values, which corrects for bias in estimating the
variance by dividing by N-1 instead of N.
- sampleVariance() - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Compute the sample variance of this RDD's elements (which corrects for bias in
estimating the standard variance by dividing by N-1 instead of N).
- sampleVariance() - 类 中的方法org.apache.spark.rdd.DoubleRDDFunctions
-
Compute the sample variance of this RDD's elements (which corrects for bias in
estimating the variance by dividing by N-1 instead of N).
- sampleVariance() - 类 中的方法org.apache.spark.util.StatCounter
-
Return the sample variance, which corrects for bias in estimating the variance by dividing
by N-1 instead of N.
- SamplingUtils - org.apache.spark.util.random中的类
-
- SamplingUtils() - 类 的构造器org.apache.spark.util.random.SamplingUtils
-
- sanitizeDirName(String) - 类 中的静态方法org.apache.spark.util.Utils
-
- satisfy(Distribution) - 接口 中的方法org.apache.spark.sql.connector.read.partitioning.Partitioning
-
Returns true if this partitioning can satisfy the given distribution, which means Spark does
not need to shuffle the output data of this data source for some certain operations.
- save(String) - 接口 中的方法org.apache.spark.ml.util.MLWritable
-
Saves this ML instance to the input path, a shortcut of write.save(path).
- save(String) - 类 中的方法org.apache.spark.ml.util.MLWriter
-
Saves the ML instances to the input path.
- save(SparkContext, String, String, int, int, Vector, double, Option<Object>) - 类 中的方法org.apache.spark.mllib.classification.impl.GLMClassificationModel.SaveLoadV1_0$
-
Helper method for saving GLM classification model metadata and data.
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.classification.LogisticRegressionModel
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel
-
- save(SparkContext, String, org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV1_0.Data) - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV1_0$
-
- save(SparkContext, String, org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV2_0.Data) - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV2_0$
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.classification.SVMModel
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeansModel
-
- save(SparkContext, BisectingKMeansModel, String) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeansModel.SaveLoadV1_0$
-
- save(SparkContext, BisectingKMeansModel, String) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeansModel.SaveLoadV2_0$
-
- save(SparkContext, BisectingKMeansModel, String) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeansModel.SaveLoadV3_0$
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.clustering.DistributedLDAModel
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixtureModel
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.clustering.KMeansModel
-
- save(SparkContext, KMeansModel, String) - 类 中的方法org.apache.spark.mllib.clustering.KMeansModel.SaveLoadV1_0$
-
- save(SparkContext, KMeansModel, String) - 类 中的方法org.apache.spark.mllib.clustering.KMeansModel.SaveLoadV2_0$
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.clustering.LocalLDAModel
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.clustering.PowerIterationClusteringModel
-
- save(SparkContext, PowerIterationClusteringModel, String) - 类 中的方法org.apache.spark.mllib.clustering.PowerIterationClusteringModel.SaveLoadV1_0$
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelectorModel
-
- save(SparkContext, ChiSqSelectorModel, String) - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelectorModel.SaveLoadV1_0$
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.feature.Word2VecModel
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.fpm.FPGrowthModel
-
Save this model to the given path.
- save(FPGrowthModel<?>, String) - 类 中的方法org.apache.spark.mllib.fpm.FPGrowthModel.SaveLoadV1_0$
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.fpm.PrefixSpanModel
-
Save this model to the given path.
- save(PrefixSpanModel<?>, String) - 类 中的方法org.apache.spark.mllib.fpm.PrefixSpanModel.SaveLoadV1_0$
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.recommendation.MatrixFactorizationModel
-
Save this model to the given path.
- save(MatrixFactorizationModel, String) - 类 中的方法org.apache.spark.mllib.recommendation.MatrixFactorizationModel.SaveLoadV1_0$
-
Saves a
MatrixFactorizationModel, where user features are saved under
data/users and
product features are saved under
data/products.
- save(SparkContext, String, String, Vector, double) - 类 中的方法org.apache.spark.mllib.regression.impl.GLMRegressionModel.SaveLoadV1_0$
-
Helper method for saving GLM regression model metadata and data.
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.regression.IsotonicRegressionModel
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.regression.LassoModel
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.regression.LinearRegressionModel
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.regression.RidgeRegressionModel
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel
-
- save(SparkContext, String, DecisionTreeModel) - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
-
- save(SparkContext, String) - 类 中的方法org.apache.spark.mllib.tree.model.RandomForestModel
-
- save(SparkContext, String) - 接口 中的方法org.apache.spark.mllib.util.Saveable
-
Save this model to the given path.
- save(String) - 类 中的方法org.apache.spark.sql.DataFrameWriter
-
Saves the content of the DataFrame at the specified path.
- save() - 类 中的方法org.apache.spark.sql.DataFrameWriter
-
Saves the content of the DataFrame as the specified table.
- Saveable - org.apache.spark.mllib.util中的接口
-
:: DeveloperApi ::
Trait for models and transformers which may be saved as files.
- saveAsHadoopDataset(JobConf) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object for
that storage system.
- saveAsHadoopDataset(JobConf) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object for
that storage system.
- saveAsHadoopFile(String, Class<?>, Class<?>, Class<F>, JobConf) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Output the RDD to any Hadoop-supported file system.
- saveAsHadoopFile(String, Class<?>, Class<?>, Class<F>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Output the RDD to any Hadoop-supported file system.
- saveAsHadoopFile(String, Class<?>, Class<?>, Class<F>, Class<? extends CompressionCodec>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Output the RDD to any Hadoop-supported file system, compressing with the supplied codec.
- saveAsHadoopFile(String, ClassTag<F>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class
supporting the key and value types K and V in this RDD.
- saveAsHadoopFile(String, Class<? extends CompressionCodec>, ClassTag<F>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class
supporting the key and value types K and V in this RDD.
- saveAsHadoopFile(String, Class<?>, Class<?>, Class<? extends OutputFormat<?, ?>>, Class<? extends CompressionCodec>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class
supporting the key and value types K and V in this RDD.
- saveAsHadoopFile(String, Class<?>, Class<?>, Class<? extends OutputFormat<?, ?>>, JobConf, Option<Class<? extends CompressionCodec>>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Output the RDD to any Hadoop-supported file system, using a Hadoop OutputFormat class
supporting the key and value types K and V in this RDD.
- saveAsHadoopFiles(String, String) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Save each RDD in this DStream as a Hadoop file.
- saveAsHadoopFiles(String, String, Class<?>, Class<?>, Class<F>) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Save each RDD in this DStream as a Hadoop file.
- saveAsHadoopFiles(String, String, Class<?>, Class<?>, Class<F>, JobConf) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Save each RDD in this DStream as a Hadoop file.
- saveAsHadoopFiles(String, String, ClassTag<F>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Save each RDD in this DStream as a Hadoop file.
- saveAsHadoopFiles(String, String, Class<?>, Class<?>, Class<? extends OutputFormat<?, ?>>, JobConf) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Save each RDD in this DStream as a Hadoop file.
- SaveAsHiveFile - org.apache.spark.sql.hive.execution中的接口
-
- saveAsHiveFile(SparkSession, SparkPlan, Configuration, org.apache.spark.sql.hive.HiveShim.ShimFileSinkDesc, String, Map<Map<String, String>, String>, Seq<Attribute>) - 接口 中的方法org.apache.spark.sql.hive.execution.SaveAsHiveFile
-
- saveAsLibSVMFile(RDD<LabeledPoint>, String) - 类 中的静态方法org.apache.spark.mllib.util.MLUtils
-
Save labeled data in LIBSVM format.
- saveAsNewAPIHadoopDataset(Configuration) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Output the RDD to any Hadoop-supported storage system, using
a Configuration object for that storage system.
- saveAsNewAPIHadoopDataset(Configuration) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Output the RDD to any Hadoop-supported storage system with new Hadoop API, using a Hadoop
Configuration object for that storage system.
- saveAsNewAPIHadoopFile(String, Class<?>, Class<?>, Class<F>, Configuration) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Output the RDD to any Hadoop-supported file system.
- saveAsNewAPIHadoopFile(String, Class<?>, Class<?>, Class<F>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Output the RDD to any Hadoop-supported file system.
- saveAsNewAPIHadoopFile(String, ClassTag<F>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Output the RDD to any Hadoop-supported file system, using a new Hadoop API OutputFormat
(mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.
- saveAsNewAPIHadoopFile(String, Class<?>, Class<?>, Class<? extends OutputFormat<?, ?>>, Configuration) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Output the RDD to any Hadoop-supported file system, using a new Hadoop API OutputFormat
(mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.
- saveAsNewAPIHadoopFiles(String, String) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Save each RDD in this DStream as a Hadoop file.
- saveAsNewAPIHadoopFiles(String, String, Class<?>, Class<?>, Class<F>) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Save each RDD in this DStream as a Hadoop file.
- saveAsNewAPIHadoopFiles(String, String, Class<?>, Class<?>, Class<F>, Configuration) - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Save each RDD in this DStream as a Hadoop file.
- saveAsNewAPIHadoopFiles(String, String, ClassTag<F>) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Save each RDD in this DStream as a Hadoop file.
- saveAsNewAPIHadoopFiles(String, String, Class<?>, Class<?>, Class<? extends OutputFormat<?, ?>>, Configuration) - 类 中的方法org.apache.spark.streaming.dstream.PairDStreamFunctions
-
Save each RDD in this DStream as a Hadoop file.
- saveAsObjectFile(String) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Save this RDD as a SequenceFile of serialized objects.
- saveAsObjectFile(String) - 类 中的方法org.apache.spark.rdd.RDD
-
Save this RDD as a SequenceFile of serialized objects.
- saveAsObjectFiles(String, String) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Save each RDD in this DStream as a Sequence file of serialized objects.
- saveAsSequenceFile(String, Option<Class<? extends CompressionCodec>>) - 类 中的方法org.apache.spark.rdd.SequenceFileRDDFunctions
-
Output the RDD as a Hadoop SequenceFile using the Writable types we infer from the RDD's key
and value types.
- saveAsTable(String) - 类 中的方法org.apache.spark.sql.DataFrameWriter
-
Saves the content of the DataFrame as the specified table.
- saveAsTextFile(String) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Save this RDD as a text file, using string representations of elements.
- saveAsTextFile(String, Class<? extends CompressionCodec>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Save this RDD as a compressed text file, using string representations of elements.
- saveAsTextFile(String) - 类 中的方法org.apache.spark.rdd.RDD
-
Save this RDD as a text file, using string representations of elements.
- saveAsTextFile(String, Class<? extends CompressionCodec>) - 类 中的方法org.apache.spark.rdd.RDD
-
Save this RDD as a compressed text file, using string representations of elements.
- saveAsTextFiles(String, String) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Save each RDD in this DStream as at text file, using string representation
of elements.
- savedTasks() - 类 中的方法org.apache.spark.status.LiveStage
-
- saveImpl(Params, PipelineStage[], SparkContext, String) - 类 中的方法org.apache.spark.ml.Pipeline.SharedReadWrite$
-
Save metadata and stages for a
Pipeline or
PipelineModel
- save metadata to path/metadata
- save stages to stages/IDX_UID
- saveImpl(M, String, SparkSession, JsonAST.JObject) - 类 中的静态方法org.apache.spark.ml.tree.EnsembleModelReadWrite
-
Helper method for saving a tree ensemble to disk.
- SaveInstanceEnd - org.apache.spark.ml中的类
-
Event fired after MLWriter.save.
- SaveInstanceEnd(String) - 类 的构造器org.apache.spark.ml.SaveInstanceEnd
-
- SaveInstanceStart - org.apache.spark.ml中的类
-
Event fired before MLWriter.save.
- SaveInstanceStart(String) - 类 的构造器org.apache.spark.ml.SaveInstanceStart
-
- SaveLoadV1_0$() - 类 的构造器org.apache.spark.mllib.classification.impl.GLMClassificationModel.SaveLoadV1_0$
-
- SaveLoadV1_0$() - 类 的构造器org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV1_0$
-
- SaveLoadV1_0$() - 类 的构造器org.apache.spark.mllib.clustering.BisectingKMeansModel.SaveLoadV1_0$
-
- SaveLoadV1_0$() - 类 的构造器org.apache.spark.mllib.clustering.KMeansModel.SaveLoadV1_0$
-
- SaveLoadV1_0$() - 类 的构造器org.apache.spark.mllib.clustering.PowerIterationClusteringModel.SaveLoadV1_0$
-
- SaveLoadV1_0$() - 类 的构造器org.apache.spark.mllib.feature.ChiSqSelectorModel.SaveLoadV1_0$
-
- SaveLoadV1_0$() - 类 的构造器org.apache.spark.mllib.fpm.FPGrowthModel.SaveLoadV1_0$
-
- SaveLoadV1_0$() - 类 的构造器org.apache.spark.mllib.fpm.PrefixSpanModel.SaveLoadV1_0$
-
- SaveLoadV1_0$() - 类 的构造器org.apache.spark.mllib.recommendation.MatrixFactorizationModel.SaveLoadV1_0$
-
- SaveLoadV1_0$() - 类 的构造器org.apache.spark.mllib.regression.impl.GLMRegressionModel.SaveLoadV1_0$
-
- SaveLoadV1_0$() - 类 的构造器org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$
-
- SaveLoadV2_0$() - 类 的构造器org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV2_0$
-
- SaveLoadV2_0$() - 类 的构造器org.apache.spark.mllib.clustering.BisectingKMeansModel.SaveLoadV2_0$
-
- SaveLoadV2_0$() - 类 的构造器org.apache.spark.mllib.clustering.KMeansModel.SaveLoadV2_0$
-
- SaveLoadV3_0$() - 类 的构造器org.apache.spark.mllib.clustering.BisectingKMeansModel.SaveLoadV3_0$
-
- SaveMode - org.apache.spark.sql中的枚举
-
SaveMode is used to specify the expected behavior of saving a DataFrame to a data source.
- sc() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
- sc() - 接口 中的方法org.apache.spark.ml.util.BaseReadWrite
-
Returns the underlying `SparkContext`.
- sc() - 类 中的方法org.apache.spark.sql.SQLImplicits.StringToColumn
-
- scal(double, Vector) - 类 中的静态方法org.apache.spark.ml.linalg.BLAS
-
x = a * x
- scal(double, Vector) - 类 中的静态方法org.apache.spark.mllib.linalg.BLAS
-
x = a * x
- scalaBoolean() - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for Scala's primitive boolean type.
- scalaByte() - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for Scala's primitive byte type.
- scalaDouble() - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for Scala's primitive double type.
- scalaFloat() - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for Scala's primitive float type.
- scalaInt() - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for Scala's primitive int type.
- scalaIntToJavaLong(DStream<Object>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
- scalaLong() - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for Scala's primitive long type.
- scalaShort() - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for Scala's primitive short type.
- scalaToJavaLong(JavaPairDStream<K, Object>, ClassTag<K>) - 类 中的静态方法org.apache.spark.streaming.api.java.JavaPairDStream
-
- scalaVersion() - 类 中的方法org.apache.spark.status.api.v1.RuntimeInfo
-
- scale() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- scale() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionModel
-
- scale() - 类 中的方法org.apache.spark.mllib.random.GammaGenerator
-
- scale() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- scale() - 类 中的方法org.apache.spark.sql.types.DecimalType
-
- scalingVec() - 类 中的方法org.apache.spark.ml.feature.ElementwiseProduct
-
the vector to multiply with input vectors
- scalingVec() - 类 中的方法org.apache.spark.mllib.feature.ElementwiseProduct
-
- Scan - org.apache.spark.sql.connector.read中的接口
-
A logical representation of a data source scan.
- ScanBuilder - org.apache.spark.sql.connector.read中的接口
-
An interface for building the
Scan.
- Schedulable - org.apache.spark.scheduler中的接口
-
An interface for schedulable entities.
- SchedulableBuilder - org.apache.spark.scheduler中的接口
-
An interface to build Schedulable tree
buildPools: build the tree nodes(pools)
addTaskSetManager: build the leaf nodes(TaskSetManagers)
- schedulableQueue() - 接口 中的方法org.apache.spark.scheduler.Schedulable
-
- SCHEDULED() - 类 中的静态方法org.apache.spark.streaming.scheduler.ReceiverState
-
- SCHEDULER_DELAY() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- SCHEDULER_DELAY() - 类 中的静态方法org.apache.spark.ui.jobs.TaskDetailsClassNames
-
- SCHEDULER_DELAY() - 类 中的静态方法org.apache.spark.ui.ToolTips
-
- SchedulerBackend - org.apache.spark.scheduler中的接口
-
A backend interface for scheduling systems that allows plugging in different ones under
TaskSchedulerImpl.
- SchedulerBackendUtils - org.apache.spark.scheduler.cluster中的类
-
- SchedulerBackendUtils() - 类 的构造器org.apache.spark.scheduler.cluster.SchedulerBackendUtils
-
- schedulerDelay() - 类 中的方法org.apache.spark.status.api.v1.TaskData
-
- schedulerDelay() - 类 中的方法org.apache.spark.status.api.v1.TaskMetricDistributions
-
- schedulerDelay(TaskData) - 类 中的静态方法org.apache.spark.status.AppStatusUtils
-
- schedulerDelay(long, long, long, long, long, long) - 类 中的静态方法org.apache.spark.status.AppStatusUtils
-
- SchedulerPool - org.apache.spark.status中的类
-
- SchedulerPool(String) - 类 的构造器org.apache.spark.status.SchedulerPool
-
- SchedulingAlgorithm - org.apache.spark.scheduler中的接口
-
An interface for sort algorithm
FIFO: FIFO algorithm between TaskSetManagers
FS: FS algorithm between Pools, and FIFO or FS within Pools
- schedulingDelay() - 类 中的方法org.apache.spark.status.api.v1.streaming.BatchInfo
-
- schedulingDelay() - 类 中的方法org.apache.spark.streaming.scheduler.BatchInfo
-
Time taken for the first job of this batch to start processing from the time this batch
was submitted to the streaming scheduler.
- schedulingMode() - 接口 中的方法org.apache.spark.scheduler.Schedulable
-
- SchedulingMode - org.apache.spark.scheduler中的类
-
"FAIR" and "FIFO" determines which policy is used
to order tasks amongst a Schedulable's sub-queues
"NONE" is used when the a Schedulable has no sub-queues.
- SchedulingMode() - 类 的构造器org.apache.spark.scheduler.SchedulingMode
-
- schedulingMode() - 接口 中的方法org.apache.spark.scheduler.TaskScheduler
-
- schedulingPool() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- schedulingPool() - 类 中的方法org.apache.spark.status.LiveStage
-
- schema() - 接口 中的方法org.apache.spark.sql.connector.catalog.Table
-
Returns the schema of this table.
- schema(StructType) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Specifies the input schema.
- schema(String) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Specifies the schema by using the input DDL-formatted string.
- schema() - 类 中的方法org.apache.spark.sql.Dataset
-
Returns the schema of this Dataset.
- schema() - 接口 中的方法org.apache.spark.sql.Encoder
-
Returns the schema of encoding this type of object as a Row.
- schema() - 接口 中的方法org.apache.spark.sql.Row
-
Schema for the row.
- schema() - 类 中的方法org.apache.spark.sql.sources.BaseRelation
-
- schema(StructType) - 类 中的方法org.apache.spark.sql.streaming.DataStreamReader
-
Specifies the input schema.
- schema(String) - 类 中的方法org.apache.spark.sql.streaming.DataStreamReader
-
Specifies the schema by using the input DDL-formatted string.
- schema_of_csv(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Parses a CSV string and infers its schema in DDL format.
- schema_of_csv(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Parses a CSV string and infers its schema in DDL format.
- schema_of_csv(Column, Map<String, String>) - 类 中的静态方法org.apache.spark.sql.functions
-
Parses a CSV string and infers its schema in DDL format using options.
- schema_of_json(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Parses a JSON string and infers its schema in DDL format.
- schema_of_json(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Parses a JSON string and infers its schema in DDL format.
- schema_of_json(Column, Map<String, String>) - 类 中的静态方法org.apache.spark.sql.functions
-
Parses a JSON string and infers its schema in DDL format using options.
- schemaLess() - 类 中的方法org.apache.spark.sql.hive.execution.HiveScriptIOSchema
-
- SchemaRelationProvider - org.apache.spark.sql.sources中的接口
-
Implemented by objects that produce relations for a specific kind of data source
with a given schema.
- SchemaUtils - org.apache.spark.ml.util中的类
-
Utils for handling schemas.
- SchemaUtils() - 类 的构造器org.apache.spark.ml.util.SchemaUtils
-
- SchemaUtils - org.apache.spark.sql.util中的类
-
Utils for handling schemas.
- SchemaUtils() - 类 的构造器org.apache.spark.sql.util.SchemaUtils
-
- scope() - 类 中的方法org.apache.spark.storage.RDDInfo
-
- scoreAndLabels() - 类 中的方法org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
-
- scoreLabelsWeight() - 类 中的方法org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
-
- scratch() - 类 中的方法org.apache.spark.mllib.optimization.NNLS.Workspace
-
- script() - 类 中的方法org.apache.spark.sql.hive.execution.ScriptTransformationExec
-
- Scripts() - 接口 中的方法org.apache.spark.sql.hive.HiveStrategies
-
- Scripts() - 类 的构造器org.apache.spark.sql.hive.HiveStrategies.Scripts
-
- Scripts$() - 类 的构造器org.apache.spark.sql.hive.HiveStrategies.Scripts$
-
- ScriptTransformationExec - org.apache.spark.sql.hive.execution中的类
-
Transforms the input by forking and running the specified script.
- ScriptTransformationExec(Seq<Expression>, String, Seq<Attribute>, SparkPlan, HiveScriptIOSchema) - 类 的构造器org.apache.spark.sql.hive.execution.ScriptTransformationExec
-
- ScriptTransformationWriterThread - org.apache.spark.sql.hive.execution中的类
-
- ScriptTransformationWriterThread(Iterator<InternalRow>, Seq<DataType>, org.apache.spark.sql.catalyst.expressions.Projection, AbstractSerDe, ObjectInspector, HiveScriptIOSchema, OutputStream, Process, org.apache.spark.util.CircularBuffer, TaskContext, Configuration) - 类 的构造器org.apache.spark.sql.hive.execution.ScriptTransformationWriterThread
-
- second(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Extracts the seconds as an integer from a given date/timestamp/string.
- seconds() - 类 中的静态方法org.apache.spark.scheduler.StatsReportListener
-
- seconds(long) - 类 中的静态方法org.apache.spark.streaming.Durations
-
- Seconds - org.apache.spark.streaming中的类
-
Helper object that creates instance of
Duration representing
a given number of seconds.
- Seconds() - 类 的构造器org.apache.spark.streaming.Seconds
-
- securityManager() - 类 中的方法org.apache.spark.SparkEnv
-
- securityManager() - 接口 中的方法org.apache.spark.status.api.v1.UIRoot
-
- seed() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- seed() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- seed() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- seed() - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- seed() - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
- seed() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- seed() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- seed() - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeans
-
- seed() - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
- seed() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixture
-
- seed() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- seed() - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- seed() - 类 中的方法org.apache.spark.ml.clustering.KMeansModel
-
- seed() - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- seed() - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
- seed() - 类 中的方法org.apache.spark.ml.feature.BucketedRandomProjectionLSH
-
- seed() - 类 中的方法org.apache.spark.ml.feature.MinHashLSH
-
- seed() - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- seed() - 类 中的方法org.apache.spark.ml.feature.Word2VecModel
-
- seed() - 接口 中的方法org.apache.spark.ml.param.shared.HasSeed
-
Param for random seed.
- seed() - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- seed() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- seed() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- seed() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- seed() - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- seed() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- seed() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- seed() - 类 中的方法org.apache.spark.ml.tuning.CrossValidator
-
- seed() - 类 中的方法org.apache.spark.ml.tuning.CrossValidatorModel
-
- seed() - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplit
-
- seed() - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplitModel
-
- seedParam() - 类 中的静态方法org.apache.spark.ml.image.SamplePathFilter
-
- select(Column...) - 类 中的方法org.apache.spark.sql.Dataset
-
Selects a set of column based expressions.
- select(String, String...) - 类 中的方法org.apache.spark.sql.Dataset
-
Selects a set of columns.
- select(Seq<Column>) - 类 中的方法org.apache.spark.sql.Dataset
-
Selects a set of column based expressions.
- select(String, Seq<String>) - 类 中的方法org.apache.spark.sql.Dataset
-
Selects a set of columns.
- select(TypedColumn<T, U1>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset by computing the given
Column expression for each element.
- select(TypedColumn<T, U1>, TypedColumn<T, U2>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset by computing the given
Column expressions for each element.
- select(TypedColumn<T, U1>, TypedColumn<T, U2>, TypedColumn<T, U3>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset by computing the given
Column expressions for each element.
- select(TypedColumn<T, U1>, TypedColumn<T, U2>, TypedColumn<T, U3>, TypedColumn<T, U4>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset by computing the given
Column expressions for each element.
- select(TypedColumn<T, U1>, TypedColumn<T, U2>, TypedColumn<T, U3>, TypedColumn<T, U4>, TypedColumn<T, U5>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset by computing the given
Column expressions for each element.
- selectedFeatures() - 类 中的方法org.apache.spark.ml.feature.ChiSqSelectorModel
-
list of indices to select (filter).
- selectedFeatures() - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelectorModel
-
- selectExpr(String...) - 类 中的方法org.apache.spark.sql.Dataset
-
Selects a set of SQL expressions.
- selectExpr(Seq<String>) - 类 中的方法org.apache.spark.sql.Dataset
-
Selects a set of SQL expressions.
- selectorType() - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- selectorType() - 类 中的方法org.apache.spark.ml.feature.ChiSqSelectorModel
-
- selectorType() - 接口 中的方法org.apache.spark.ml.feature.ChiSqSelectorParams
-
The selector type of the ChisqSelector.
- selectorType() - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelector
-
- self() - 接口 中的方法org.apache.spark.rpc.RpcEndpoint
-
- sendData(String, Seq<Object>) - 接口 中的方法org.apache.spark.streaming.kinesis.KinesisDataGenerator
-
Sends the data to Kinesis and returns the metadata for everything that has been sent.
- sender() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.RegisterBlockManager
-
- senderAddress() - 接口 中的方法org.apache.spark.rpc.RpcCallContext
-
The sender of this message.
- sendFailure(Throwable) - 接口 中的方法org.apache.spark.rpc.RpcCallContext
-
Report a failure to the sender.
- sendToDst(A) - 类 中的方法org.apache.spark.graphx.EdgeContext
-
Sends a message to the destination vertex.
- sendToDst(A) - 类 中的方法org.apache.spark.graphx.impl.AggregatingEdgeContext
-
- sendToSrc(A) - 类 中的方法org.apache.spark.graphx.EdgeContext
-
Sends a message to the source vertex.
- sendToSrc(A) - 类 中的方法org.apache.spark.graphx.impl.AggregatingEdgeContext
-
- sendWith(TransportClient) - 接口 中的方法org.apache.spark.rpc.netty.OutboxMessage
-
- seqToString(Seq<T>, Function1<T, String>) - 类 中的静态方法org.apache.spark.internal.config.ConfigHelpers
-
- sequence() - 类 中的方法org.apache.spark.mllib.fpm.PrefixSpan.FreqSequence
-
- sequence(Column, Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Generate a sequence of integers from start to stop, incrementing by step.
- sequence(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Generate a sequence of integers from start to stop,
incrementing by 1 if start is less than or equal to stop, otherwise -1.
- sequenceCol() - 类 中的方法org.apache.spark.ml.fpm.PrefixSpan
-
Param for the name of the sequence column in dataset (default "sequence"), rows with
nulls in this column are ignored.
- sequenceFile(String, Class<K>, Class<V>, int) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Get an RDD for a Hadoop SequenceFile with given key and value types.
- sequenceFile(String, Class<K>, Class<V>) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Get an RDD for a Hadoop SequenceFile.
- sequenceFile(String, Class<K>, Class<V>, int) - 类 中的方法org.apache.spark.SparkContext
-
Get an RDD for a Hadoop SequenceFile with given key and value types.
- sequenceFile(String, Class<K>, Class<V>) - 类 中的方法org.apache.spark.SparkContext
-
Get an RDD for a Hadoop SequenceFile with given key and value types.
- sequenceFile(String, int, ClassTag<K>, ClassTag<V>, Function0<WritableConverter<K>>, Function0<WritableConverter<V>>) - 类 中的方法org.apache.spark.SparkContext
-
Version of sequenceFile() for types implicitly convertible to Writables through a
WritableConverter.
- SequenceFileRDDFunctions<K,V> - org.apache.spark.rdd中的类
-
Extra functions available on RDDs of (key, value) pairs to create a Hadoop SequenceFile,
through an implicit conversion.
- SequenceFileRDDFunctions(RDD<Tuple2<K, V>>, Class<? extends Writable>, Class<? extends Writable>, Function1<K, Writable>, ClassTag<K>, Function1<V, Writable>, ClassTag<V>) - 类 的构造器org.apache.spark.rdd.SequenceFileRDDFunctions
-
- SER_TIME() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- SerDe - org.apache.spark.api.r中的类
-
Utility functions to serialize, deserialize objects to / from R
- SerDe() - 类 的构造器org.apache.spark.api.r.SerDe
-
- SERDE() - 类 中的静态方法org.apache.spark.sql.hive.execution.HiveOptions
-
- serde() - 类 中的方法org.apache.spark.sql.hive.execution.HiveOptions
-
- serdeProperties() - 类 中的方法org.apache.spark.sql.hive.execution.HiveOptions
-
- SerializableConfiguration - org.apache.spark.util中的类
-
Hadoop configuration but serializable.
- SerializableConfiguration(Configuration) - 类 的构造器org.apache.spark.util.SerializableConfiguration
-
- SerializableMapWrapper(Map<A, B>) - 类 的构造器org.apache.spark.api.java.JavaUtils.SerializableMapWrapper
-
- SerializableWritable<T extends org.apache.hadoop.io.Writable> - org.apache.spark中的类
-
- SerializableWritable(T) - 类 的构造器org.apache.spark.SerializableWritable
-
- SerializationDebugger - org.apache.spark.serializer中的类
-
- SerializationDebugger() - 类 的构造器org.apache.spark.serializer.SerializationDebugger
-
- SerializationDebugger.ObjectStreamClassMethods - org.apache.spark.serializer中的类
-
An implicit class that allows us to call private methods of ObjectStreamClass.
- SerializationDebugger.ObjectStreamClassMethods$ - org.apache.spark.serializer中的类
-
- SerializationFormats - org.apache.spark.api.r中的类
-
- SerializationFormats() - 类 的构造器org.apache.spark.api.r.SerializationFormats
-
- SerializationStream - org.apache.spark.serializer中的类
-
:: DeveloperApi ::
A stream for writing serialized objects.
- SerializationStream() - 类 的构造器org.apache.spark.serializer.SerializationStream
-
- serializationStream() - 类 中的方法org.apache.spark.storage.memory.SerializedValuesHolder
-
- serialize(Vector) - 类 中的方法org.apache.spark.mllib.linalg.VectorUDT
-
- serialize(T, ClassTag<T>) - 类 中的方法org.apache.spark.serializer.DummySerializerInstance
-
- serialize(T, ClassTag<T>) - 类 中的方法org.apache.spark.serializer.SerializerInstance
-
- serialize(T) - 类 中的静态方法org.apache.spark.util.Utils
-
Serialize an object using Java serialization
- SERIALIZED_R_DATA_SCHEMA() - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
- serializedData() - 类 中的方法org.apache.spark.scheduler.local.StatusUpdate
-
- serializedMapStatus(org.apache.spark.broadcast.BroadcastManager, boolean, int, SparkConf) - 类 中的方法org.apache.spark.ShuffleStatus
-
Serializes the mapStatuses array into an efficient compressed format.
- SerializedMemoryEntry<T> - org.apache.spark.storage.memory中的类
-
- SerializedMemoryEntry(org.apache.spark.util.io.ChunkedByteBuffer, MemoryMode, ClassTag<T>) - 类 的构造器org.apache.spark.storage.memory.SerializedMemoryEntry
-
- SerializedValuesHolder<T> - org.apache.spark.storage.memory中的类
-
A holder for storing the serialized values.
- SerializedValuesHolder(BlockId, int, ClassTag<T>, MemoryMode, org.apache.spark.serializer.SerializerManager) - 类 的构造器org.apache.spark.storage.memory.SerializedValuesHolder
-
- Serializer - org.apache.spark.serializer中的类
-
:: DeveloperApi ::
A serializer.
- Serializer() - 类 的构造器org.apache.spark.serializer.Serializer
-
- serializer() - 类 中的方法org.apache.spark.ShuffleDependency
-
- serializer() - 类 中的方法org.apache.spark.SparkEnv
-
- SerializerInstance - org.apache.spark.serializer中的类
-
:: DeveloperApi ::
An instance of a serializer, for use by one thread at a time.
- SerializerInstance() - 类 的构造器org.apache.spark.serializer.SerializerInstance
-
- serializerManager() - 类 中的方法org.apache.spark.SparkEnv
-
- serializeStream(OutputStream) - 类 中的方法org.apache.spark.serializer.DummySerializerInstance
-
- serializeStream(OutputStream) - 类 中的方法org.apache.spark.serializer.SerializerInstance
-
- serializeViaNestedStream(OutputStream, SerializerInstance, Function1<SerializationStream, BoxedUnit>) - 类 中的静态方法org.apache.spark.util.Utils
-
Serialize via nested stream using specific serializer
- serviceName() - 接口 中的方法org.apache.spark.security.HadoopDelegationTokenProvider
-
Name of the service to provide delegation tokens.
- servletContext() - 接口 中的方法org.apache.spark.status.api.v1.ApiRequestContext
-
- ServletParams(Function1<HttpServletRequest, T>, String, Function1<T, String>) - 类 的构造器org.apache.spark.ui.JettyUtils.ServletParams
-
- ServletParams$() - 类 的构造器org.apache.spark.ui.JettyUtils.ServletParams$
-
- session(SparkSession) - 类 中的静态方法org.apache.spark.ml.r.RWrappers
-
- session(SparkSession) - 接口 中的方法org.apache.spark.ml.util.BaseReadWrite
-
Sets the Spark Session to use for saving/loading.
- session(SparkSession) - 类 中的方法org.apache.spark.ml.util.GeneralMLWriter
-
- session(SparkSession) - 类 中的方法org.apache.spark.ml.util.MLReader
-
- session(SparkSession) - 类 中的方法org.apache.spark.ml.util.MLWriter
-
- sessionCatalog() - 类 中的方法org.apache.spark.sql.hive.RelationConversions
-
- SessionConfigSupport - org.apache.spark.sql.connector.catalog中的接口
-
- sessionState() - 类 中的方法org.apache.spark.sql.SparkSession
-
- set(long, long, int, int, VD, VD, ED) - 类 中的方法org.apache.spark.graphx.impl.AggregatingEdgeContext
-
- Set() - 类 中的静态方法org.apache.spark.metrics.sink.StatsdMetricType
-
- set(Param<T>, T) - 接口 中的方法org.apache.spark.ml.param.Params
-
Sets a parameter in the embedded param map.
- set(String, Object) - 接口 中的方法org.apache.spark.ml.param.Params
-
Sets a parameter (by name) in the embedded param map.
- set(ParamPair<?>) - 接口 中的方法org.apache.spark.ml.param.Params
-
Sets a parameter in the embedded param map.
- set(String, long, long) - 类 中的静态方法org.apache.spark.rdd.InputFileBlockHolder
-
Sets the thread-local input block.
- set(String, String) - 类 中的方法org.apache.spark.SparkConf
-
Set a configuration variable.
- set(SparkEnv) - 类 中的静态方法org.apache.spark.SparkEnv
-
- set(String, String) - 类 中的方法org.apache.spark.sql.RuntimeConfig
-
Sets the given Spark runtime configuration property.
- set(String, boolean) - 类 中的方法org.apache.spark.sql.RuntimeConfig
-
Sets the given Spark runtime configuration property.
- set(String, long) - 类 中的方法org.apache.spark.sql.RuntimeConfig
-
Sets the given Spark runtime configuration property.
- set(long) - 类 中的方法org.apache.spark.sql.types.Decimal
-
Set this Decimal to the given Long.
- set(int) - 类 中的方法org.apache.spark.sql.types.Decimal
-
Set this Decimal to the given Int.
- set(long, int, int) - 类 中的方法org.apache.spark.sql.types.Decimal
-
Set this Decimal to the given unscaled Long, with a given precision and scale.
- set(BigDecimal, int, int) - 类 中的方法org.apache.spark.sql.types.Decimal
-
Set this Decimal to the given BigDecimal value, with a given precision and scale.
- set(BigDecimal) - 类 中的方法org.apache.spark.sql.types.Decimal
-
Set this Decimal to the given BigDecimal value, inheriting its precision and scale.
- set(BigInteger) - 类 中的方法org.apache.spark.sql.types.Decimal
-
If the value is not in the range of long, convert it to BigDecimal and
the precision and scale are based on the converted value.
- set(Decimal) - 类 中的方法org.apache.spark.sql.types.Decimal
-
Set this Decimal to the given Decimal value.
- setActiveSession(SparkSession) - 类 中的静态方法org.apache.spark.sql.SparkSession
-
Changes the SparkSession that will be returned in this thread and its children when
SparkSession.getOrCreate() is called.
- setAggregationDepth(int) - 类 中的方法org.apache.spark.ml.classification.LinearSVC
-
Suggested depth for treeAggregate (greater than or equal to 2).
- setAggregationDepth(int) - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
Suggested depth for treeAggregate (greater than or equal to 2).
- setAggregationDepth(int) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
Suggested depth for treeAggregate (greater than or equal to 2).
- setAggregationDepth(int) - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
Suggested depth for treeAggregate (greater than or equal to 2).
- setAggregator(Aggregator<K, V, C>) - 类 中的方法org.apache.spark.rdd.ShuffledRDD
-
Set aggregator for RDD's shuffle.
- setAlgo(String) - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
Sets Algorithm using a String.
- setAlgo(Enumeration.Value) - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- setAll(Iterable<Tuple2<String, String>>) - 类 中的方法org.apache.spark.SparkConf
-
Set multiple parameters together
- setAll(Traversable<Tuple2<String, String>>) - 类 中的方法org.apache.spark.SparkConf
-
- setAlpha(double) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setAlpha(Vector) - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Alias for setDocConcentration()
- setAlpha(double) - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Alias for setDocConcentration()
- setAlpha(double) - 类 中的方法org.apache.spark.mllib.recommendation.ALS
-
Sets the constant used in computing confidence in implicit ALS.
- setAppName(String) - 类 中的方法org.apache.spark.launcher.AbstractLauncher
-
Set the application name.
- setAppName(String) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
- setAppName(String) - 类 中的方法org.apache.spark.SparkConf
-
Set a name for your application.
- setAppResource(String) - 类 中的方法org.apache.spark.launcher.AbstractLauncher
-
Set the main application resource.
- setAppResource(String) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
- setBandwidth(double) - 类 中的方法org.apache.spark.mllib.stat.KernelDensity
-
Sets the bandwidth (standard deviation) of the Gaussian kernel (default: 1.0).
- setBeta(double) - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- setBeta(double) - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Alias for setTopicConcentration()
- setBinary(boolean) - 类 中的方法org.apache.spark.ml.feature.CountVectorizer
-
- setBinary(boolean) - 类 中的方法org.apache.spark.ml.feature.CountVectorizerModel
-
- setBinary(boolean) - 类 中的方法org.apache.spark.ml.feature.HashingTF
-
- setBinary(boolean) - 类 中的方法org.apache.spark.mllib.feature.HashingTF
-
If true, term frequency vector will be binary such that non-zero term counts will be set to 1
(default: false)
- setBlocks(int) - 类 中的方法org.apache.spark.mllib.recommendation.ALS
-
Set the number of blocks for both user blocks and product blocks to parallelize the computation
into; pass -1 for an auto-configured number of blocks.
- setBlockSize(int) - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
Sets the value of param blockSize.
- setBucketLength(double) - 类 中的方法org.apache.spark.ml.feature.BucketedRandomProjectionLSH
-
- setCacheNodeIds(boolean) - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- setCacheNodeIds(boolean) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- setCacheNodeIds(boolean) - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- setCacheNodeIds(boolean) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- setCacheNodeIds(boolean) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- setCacheNodeIds(boolean) - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- setCallSite(String) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Pass-through to SparkContext.setCallSite.
- setCallSite(String) - 类 中的方法org.apache.spark.SparkContext
-
Set the thread-local property for overriding the call sites
of actions and RDDs.
- setCaseSensitive(boolean) - 类 中的方法org.apache.spark.ml.feature.StopWordsRemover
-
- setCategoricalCols(String[]) - 类 中的方法org.apache.spark.ml.feature.FeatureHasher
-
- setCategoricalFeaturesInfo(Map<Integer, Integer>) - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
Sets categoricalFeaturesInfo using a Java Map.
- setCategoricalFeaturesInfo(Map<Object, Object>) - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- setCensorCol(String) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- setCheckpointDir(String) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Set the directory under which RDDs are going to be checkpointed.
- setCheckpointDir(String) - 类 中的方法org.apache.spark.SparkContext
-
Set the directory under which RDDs are going to be checkpointed.
- setCheckpointInterval(int) - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
Specifies how often to checkpoint the cached node IDs.
- setCheckpointInterval(int) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
Specifies how often to checkpoint the cached node IDs.
- setCheckpointInterval(int) - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
Specifies how often to checkpoint the cached node IDs.
- setCheckpointInterval(int) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- setCheckpointInterval(int) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setCheckpointInterval(int) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
Specifies how often to checkpoint the cached node IDs.
- setCheckpointInterval(int) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
Specifies how often to checkpoint the cached node IDs.
- setCheckpointInterval(int) - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
Specifies how often to checkpoint the cached node IDs.
- setCheckpointInterval(int) - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Parameter for set checkpoint interval (greater than or equal to 1) or disable checkpoint (-1).
- setCheckpointInterval(int) - 类 中的方法org.apache.spark.mllib.recommendation.ALS
-
:: DeveloperApi ::
Set period (in iterations) between checkpoints (default = 10).
- setCheckpointInterval(int) - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- setClassifier(Classifier<?, ?, ?>) - 类 中的方法org.apache.spark.ml.classification.OneVsRest
-
- setColdStartStrategy(String) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setColdStartStrategy(String) - 类 中的方法org.apache.spark.ml.recommendation.ALSModel
-
- setCollectSubModels(boolean) - 类 中的方法org.apache.spark.ml.tuning.CrossValidator
-
Whether to collect submodels when fitting.
- setCollectSubModels(boolean) - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplit
-
Whether to collect submodels when fitting.
- setConf(Configuration) - 接口 中的方法org.apache.spark.input.Configurable
-
- setConf(String, String) - 类 中的方法org.apache.spark.launcher.AbstractLauncher
-
Set a single configuration value for the application.
- setConf(String, String) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
- setConf(Configuration) - 类 中的方法org.apache.spark.ml.image.SamplePathFilter
-
- setConf(Properties) - 类 中的方法org.apache.spark.sql.SQLContext
-
Set Spark SQL configuration properties.
- setConf(String, String) - 类 中的方法org.apache.spark.sql.SQLContext
-
Set the given Spark SQL configuration property.
- setConfig(String, String) - 类 中的静态方法org.apache.spark.launcher.SparkLauncher
-
Set a configuration value for the launcher library.
- setConvergenceTol(double) - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixture
-
Set the largest change in log-likelihood at which convergence is
considered to have occurred.
- setConvergenceTol(double) - 类 中的方法org.apache.spark.mllib.optimization.GradientDescent
-
Set the convergence tolerance.
- setConvergenceTol(double) - 类 中的方法org.apache.spark.mllib.optimization.LBFGS
-
Set the convergence tolerance of iterations for L-BFGS.
- setConvergenceTol(double) - 类 中的方法org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
-
Set the convergence tolerance.
- setCurrentDatabase(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Sets the current default database in this session.
- setCurrentDatabase(String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Sets the name of current database.
- setCustomHostname(String) - 类 中的静态方法org.apache.spark.util.Utils
-
Allow setting a custom host name because when we run on Mesos we need to use the same
hostname it reports to the master.
- setDAGScheduler(DAGScheduler) - 接口 中的方法org.apache.spark.scheduler.TaskScheduler
-
- setDecayFactor(double) - 类 中的方法org.apache.spark.mllib.clustering.StreamingKMeans
-
Set the forgetfulness of the previous centroids.
- setDefault(Param<T>, T) - 接口 中的方法org.apache.spark.ml.param.Params
-
Sets a default value for a param.
- setDefault(Seq<ParamPair<?>>) - 接口 中的方法org.apache.spark.ml.param.Params
-
Sets default values for a list of params.
- setDefaultClassLoader(ClassLoader) - 类 中的方法org.apache.spark.serializer.KryoSerializer
-
- setDefaultClassLoader(ClassLoader) - 类 中的方法org.apache.spark.serializer.Serializer
-
Sets a class loader for the serializer to use in deserialization.
- setDefaultSession(SparkSession) - 类 中的静态方法org.apache.spark.sql.SparkSession
-
Sets the default SparkSession that is returned by the builder.
- setDegree(int) - 类 中的方法org.apache.spark.ml.feature.PolynomialExpansion
-
- setDelegateCatalog(CatalogPlugin) - 接口 中的方法org.apache.spark.sql.connector.catalog.CatalogExtension
-
- setDelegateCatalog(CatalogPlugin) - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- setDeployMode(String) - 类 中的方法org.apache.spark.launcher.AbstractLauncher
-
Set the deploy mode for the application.
- setDeployMode(String) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
- setDistanceMeasure(String) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeans
-
- setDistanceMeasure(String) - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- setDistanceMeasure(String) - 类 中的方法org.apache.spark.ml.evaluation.ClusteringEvaluator
-
- setDistanceMeasure(String) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeans
-
Set the distance suite used by the algorithm.
- setDistanceMeasure(String) - 类 中的方法org.apache.spark.mllib.clustering.KMeans
-
Set the distance suite used by the algorithm.
- setDocConcentration(double[]) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- setDocConcentration(double) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- setDocConcentration(Vector) - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Concentration parameter (commonly named "alpha") for the prior placed on documents'
distributions over topics ("theta").
- setDocConcentration(double) - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Replicates a Double docConcentration to create a symmetric prior.
- setDropLast(boolean) - 类 中的方法org.apache.spark.ml.feature.OneHotEncoder
-
- setDropLast(boolean) - 类 中的方法org.apache.spark.ml.feature.OneHotEncoderModel
-
- setDstCol(String) - 类 中的方法org.apache.spark.ml.clustering.PowerIterationClustering
-
- setElasticNetParam(double) - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
Set the ElasticNet mixing parameter.
- setElasticNetParam(double) - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
Set the ElasticNet mixing parameter.
- setEps(double) - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- setEpsilon(double) - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
Sets the value of param epsilon.
- setEpsilon(double) - 类 中的方法org.apache.spark.mllib.clustering.KMeans
-
Set the distance threshold within which we've consider centers to have converged.
- setError(PrintStream) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
- setEstimator(Estimator<?>) - 类 中的方法org.apache.spark.ml.tuning.CrossValidator
-
- setEstimator(Estimator<?>) - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplit
-
- setEstimatorParamMaps(ParamMap[]) - 类 中的方法org.apache.spark.ml.tuning.CrossValidator
-
- setEstimatorParamMaps(ParamMap[]) - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplit
-
- setEvaluator(Evaluator) - 类 中的方法org.apache.spark.ml.tuning.CrossValidator
-
- setEvaluator(Evaluator) - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplit
-
- setExecutorEnv(String, String) - 类 中的方法org.apache.spark.SparkConf
-
Set an environment variable to be used when launching executors for this application.
- setExecutorEnv(Seq<Tuple2<String, String>>) - 类 中的方法org.apache.spark.SparkConf
-
Set multiple environment variables to be used when launching executors.
- setExecutorEnv(Tuple2<String, String>[]) - 类 中的方法org.apache.spark.SparkConf
-
Set multiple environment variables to be used when launching executors.
- setFamily(String) - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
Sets the value of param family.
- setFamily(String) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
Sets the value of param family.
- setFdr(double) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- setFdr(double) - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelector
-
- setFeatureIndex(int) - 类 中的方法org.apache.spark.ml.regression.IsotonicRegression
-
- setFeatureIndex(int) - 类 中的方法org.apache.spark.ml.regression.IsotonicRegressionModel
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.classification.OneVsRest
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.classification.OneVsRestModel
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeans
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixture
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.clustering.KMeansModel
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
The features for LDA should be a Vector representing the word counts in a document.
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
The features for LDA should be a Vector representing the word counts in a document.
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.evaluation.ClusteringEvaluator
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelectorModel
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.feature.RFormula
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.PredictionModel
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.Predictor
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.regression.IsotonicRegression
-
- setFeaturesCol(String) - 类 中的方法org.apache.spark.ml.regression.IsotonicRegressionModel
-
- setFeatureSubsetStrategy(String) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- setFeatureSubsetStrategy(String) - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- setFeatureSubsetStrategy(String) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- setFeatureSubsetStrategy(String) - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- setFinalRDDStorageLevel(StorageLevel) - 类 中的方法org.apache.spark.mllib.recommendation.ALS
-
:: DeveloperApi ::
Sets storage level for final RDDs (user/product used in MatrixFactorizationModel).
- setFinalStorageLevel(String) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setFitIntercept(boolean) - 类 中的方法org.apache.spark.ml.classification.LinearSVC
-
Whether to fit an intercept term.
- setFitIntercept(boolean) - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
Whether to fit an intercept term.
- setFitIntercept(boolean) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
Set if we should fit the intercept
Default is true.
- setFitIntercept(boolean) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
Sets if we should fit the intercept.
- setFitIntercept(boolean) - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
Set if we should fit the intercept.
- setForceIndexLabel(boolean) - 类 中的方法org.apache.spark.ml.feature.RFormula
-
- setFormula(String) - 类 中的方法org.apache.spark.ml.feature.RFormula
-
Sets the formula to use for this transformer.
- setFpr(double) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- setFpr(double) - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelector
-
- setFwe(double) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- setFwe(double) - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelector
-
- setGaps(boolean) - 类 中的方法org.apache.spark.ml.feature.RegexTokenizer
-
- setGradient(Gradient) - 类 中的方法org.apache.spark.mllib.optimization.GradientDescent
-
Set the gradient function (of the loss function of one single data example)
to be used for SGD.
- setGradient(Gradient) - 类 中的方法org.apache.spark.mllib.optimization.LBFGS
-
Set the gradient function (of the loss function of one single data example)
to be used for L-BFGS.
- setHalfLife(double, String) - 类 中的方法org.apache.spark.mllib.clustering.StreamingKMeans
-
Set the half life and time unit ("batches" or "points").
- setHandleInvalid(String) - 类 中的方法org.apache.spark.ml.feature.Bucketizer
-
- setHandleInvalid(String) - 类 中的方法org.apache.spark.ml.feature.OneHotEncoder
-
- setHandleInvalid(String) - 类 中的方法org.apache.spark.ml.feature.OneHotEncoderModel
-
- setHandleInvalid(String) - 类 中的方法org.apache.spark.ml.feature.QuantileDiscretizer
-
- setHandleInvalid(String) - 类 中的方法org.apache.spark.ml.feature.RFormula
-
- setHandleInvalid(String) - 类 中的方法org.apache.spark.ml.feature.StringIndexer
-
- setHandleInvalid(String) - 类 中的方法org.apache.spark.ml.feature.StringIndexerModel
-
- setHandleInvalid(String) - 类 中的方法org.apache.spark.ml.feature.VectorAssembler
-
- setHandleInvalid(String) - 类 中的方法org.apache.spark.ml.feature.VectorIndexer
-
- setHandleInvalid(String) - 类 中的方法org.apache.spark.ml.feature.VectorSizeHint
-
- setHashAlgorithm(String) - 类 中的方法org.apache.spark.mllib.feature.HashingTF
-
Set the hash algorithm used when mapping term to integer.
- setIfMissing(String, String) - 类 中的方法org.apache.spark.SparkConf
-
Set a parameter if it isn't already configured
- setImplicitPrefs(boolean) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setImplicitPrefs(boolean) - 类 中的方法org.apache.spark.mllib.recommendation.ALS
-
Sets whether to use implicit preference.
- setImpurity(String) - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- setImpurity(String) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
The impurity setting is ignored for GBT models.
- setImpurity(String) - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- setImpurity(String) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- setImpurity(String) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
The impurity setting is ignored for GBT models.
- setImpurity(String) - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- setImpurity(Impurity) - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- setIndices(int[]) - 类 中的方法org.apache.spark.ml.feature.VectorSlicer
-
- setInfo(PrintStream) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
- setInitialCenters(Vector[], double[]) - 类 中的方法org.apache.spark.mllib.clustering.StreamingKMeans
-
Specify initial centers directly.
- setInitializationMode(String) - 类 中的方法org.apache.spark.mllib.clustering.KMeans
-
Set the initialization algorithm.
- setInitializationMode(String) - 类 中的方法org.apache.spark.mllib.clustering.PowerIterationClustering
-
Set the initialization mode.
- setInitializationSteps(int) - 类 中的方法org.apache.spark.mllib.clustering.KMeans
-
Set the number of steps for the k-means|| initialization mode.
- setInitialModel(GaussianMixtureModel) - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixture
-
Set the initial GMM starting point, bypassing the random initialization.
- setInitialModel(KMeansModel) - 类 中的方法org.apache.spark.mllib.clustering.KMeans
-
Set the initial starting point, bypassing the random initialization or k-means||
The condition model.k == this.k must be met, failure results
in an IllegalArgumentException.
- setInitialWeights(Vector) - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
Sets the value of param initialWeights.
- setInitialWeights(Vector) - 类 中的方法org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD
-
Set the initial weights.
- setInitialWeights(Vector) - 类 中的方法org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
-
Set the initial weights.
- setInitMode(String) - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- setInitMode(String) - 类 中的方法org.apache.spark.ml.clustering.PowerIterationClustering
-
- setInitSteps(int) - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.Binarizer
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.BucketedRandomProjectionLSH
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.BucketedRandomProjectionLSHModel
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.Bucketizer
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.CountVectorizer
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.CountVectorizerModel
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.HashingTF
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.IDF
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.IDFModel
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.Imputer
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.ImputerModel
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.IndexToString
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.MaxAbsScaler
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.MaxAbsScalerModel
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.MinHashLSH
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.MinHashLSHModel
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.MinMaxScaler
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.MinMaxScalerModel
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.OneHotEncoder
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.PCA
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.PCAModel
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.QuantileDiscretizer
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.RobustScaler
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.RobustScalerModel
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.StandardScaler
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.StandardScalerModel
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.StopWordsRemover
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.StringIndexer
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.StringIndexerModel
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.VectorIndexer
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.VectorIndexerModel
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.VectorSizeHint
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.VectorSlicer
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.feature.Word2VecModel
-
- setInputCol(String) - 类 中的方法org.apache.spark.ml.UnaryTransformer
-
- setInputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.Binarizer
-
- setInputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.Bucketizer
-
- setInputCols(Seq<String>) - 类 中的方法org.apache.spark.ml.feature.FeatureHasher
-
- setInputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.FeatureHasher
-
- setInputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.Imputer
-
- setInputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.ImputerModel
-
- setInputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.Interaction
-
- setInputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.OneHotEncoder
-
- setInputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.OneHotEncoderModel
-
- setInputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.QuantileDiscretizer
-
- setInputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.StringIndexer
-
- setInputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.StringIndexerModel
-
- setInputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.VectorAssembler
-
- setIntercept(boolean) - 类 中的方法org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
-
Set if the algorithm should add an intercept.
- setIntermediateRDDStorageLevel(StorageLevel) - 类 中的方法org.apache.spark.mllib.recommendation.ALS
-
:: DeveloperApi ::
Sets storage level for intermediate RDDs (user/product in/out links).
- setIntermediateStorageLevel(String) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setInverse(boolean) - 类 中的方法org.apache.spark.ml.feature.DCT
-
- setIsotonic(boolean) - 类 中的方法org.apache.spark.ml.regression.IsotonicRegression
-
- setIsotonic(boolean) - 类 中的方法org.apache.spark.mllib.regression.IsotonicRegression
-
Sets the isotonic parameter.
- setItemCol(String) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setItemCol(String) - 类 中的方法org.apache.spark.ml.recommendation.ALSModel
-
- setItemsCol(String) - 类 中的方法org.apache.spark.ml.fpm.FPGrowth
-
- setItemsCol(String) - 类 中的方法org.apache.spark.ml.fpm.FPGrowthModel
-
- setIterations(int) - 类 中的方法org.apache.spark.mllib.recommendation.ALS
-
Set the number of iterations to run.
- setJars(Seq<String>) - 类 中的方法org.apache.spark.SparkConf
-
Set JAR files to distribute to the cluster.
- setJars(String[]) - 类 中的方法org.apache.spark.SparkConf
-
Set JAR files to distribute to the cluster.
- setJavaHome(String) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
Set a custom JAVA_HOME for launching the Spark application.
- setJobDescription(String) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Set a human readable description of the current job.
- setJobDescription(String) - 类 中的方法org.apache.spark.SparkContext
-
Set a human readable description of the current job.
- setJobGroup(String, String, boolean) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Assigns a group ID to all the jobs started by this thread until the group ID is set to a
different value or cleared.
- setJobGroup(String, String) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Assigns a group ID to all the jobs started by this thread until the group ID is set to a
different value or cleared.
- setJobGroup(String, String, boolean) - 类 中的方法org.apache.spark.SparkContext
-
Assigns a group ID to all the jobs started by this thread until the group ID is set to a
different value or cleared.
- setK(int) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeans
-
- setK(int) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixture
-
- setK(int) - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- setK(int) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- setK(int) - 类 中的方法org.apache.spark.ml.clustering.PowerIterationClustering
-
- setK(int) - 类 中的方法org.apache.spark.ml.evaluation.RankingEvaluator
-
- setK(int) - 类 中的方法org.apache.spark.ml.feature.PCA
-
- setK(int) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeans
-
Sets the desired number of leaf clusters (default: 4).
- setK(int) - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixture
-
Set the number of Gaussians in the mixture model.
- setK(int) - 类 中的方法org.apache.spark.mllib.clustering.KMeans
-
Set the number of clusters to create (k).
- setK(int) - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Set the number of topics to infer, i.e., the number of soft cluster centers.
- setK(int) - 类 中的方法org.apache.spark.mllib.clustering.PowerIterationClustering
-
Set the number of clusters.
- setK(int) - 类 中的方法org.apache.spark.mllib.clustering.StreamingKMeans
-
Set the number of clusters.
- setKappa(double) - 类 中的方法org.apache.spark.mllib.clustering.OnlineLDAOptimizer
-
Learning rate: exponential decay rate---should be between
(0.5, 1.0] to guarantee asymptotic convergence.
- setKeepLastCheckpoint(boolean) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- setKeepLastCheckpoint(boolean) - 类 中的方法org.apache.spark.mllib.clustering.EMLDAOptimizer
-
If using checkpointing, this indicates whether to keep the last checkpoint (vs clean up).
- setKeyOrdering(Ordering<K>) - 类 中的方法org.apache.spark.rdd.ShuffledRDD
-
Set key ordering for RDD's shuffle.
- setLabelCol(String) - 类 中的方法org.apache.spark.ml.classification.OneVsRest
-
- setLabelCol(String) - 类 中的方法org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
-
- setLabelCol(String) - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- setLabelCol(String) - 类 中的方法org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator
-
- setLabelCol(String) - 类 中的方法org.apache.spark.ml.evaluation.RankingEvaluator
-
- setLabelCol(String) - 类 中的方法org.apache.spark.ml.evaluation.RegressionEvaluator
-
- setLabelCol(String) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- setLabelCol(String) - 类 中的方法org.apache.spark.ml.feature.RFormula
-
- setLabelCol(String) - 类 中的方法org.apache.spark.ml.Predictor
-
- setLabelCol(String) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- setLabelCol(String) - 类 中的方法org.apache.spark.ml.regression.IsotonicRegression
-
- setLabels(String[]) - 类 中的方法org.apache.spark.ml.feature.IndexToString
-
- setLambda(double) - 类 中的方法org.apache.spark.mllib.classification.NaiveBayes
-
Set the smoothing parameter.
- setLambda(double) - 类 中的方法org.apache.spark.mllib.recommendation.ALS
-
Set the regularization parameter, lambda.
- setLayers(int[]) - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
Sets the value of param layers.
- setLeafCol(String) - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeParams
-
- setLearningDecay(double) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- setLearningOffset(double) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- setLearningRate(double) - 类 中的方法org.apache.spark.mllib.feature.Word2Vec
-
Sets initial learning rate (default: 0.025).
- setLearningRate(double) - 类 中的方法org.apache.spark.mllib.tree.configuration.BoostingStrategy
-
- setLink(String) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
Sets the value of param link.
- setLinkPower(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
Sets the value of param linkPower.
- setLinkPredictionCol(String) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
Sets the link prediction (linear predictor) column name.
- setLinkPredictionCol(String) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
Sets the link prediction (linear predictor) column name.
- setLocale(String) - 类 中的方法org.apache.spark.ml.feature.StopWordsRemover
-
- setLocalProperty(String, String) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Set a local property that affects jobs submitted from this thread, and all child
threads, such as the Spark fair scheduler pool.
- setLocalProperty(String, String) - 类 中的方法org.apache.spark.SparkContext
-
Set a local property that affects jobs submitted from this thread, such as the Spark fair
scheduler pool.
- setLogLevel(String) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Control our logLevel.
- setLogLevel(String) - 类 中的方法org.apache.spark.SparkContext
-
Control our logLevel.
- setLogLevel(Level) - 类 中的静态方法org.apache.spark.util.Utils
-
configure a new log4j level
- setLoss(String) - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
Sets the value of param loss.
- setLoss(Loss) - 类 中的方法org.apache.spark.mllib.tree.configuration.BoostingStrategy
-
- setLossType(String) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- setLossType(String) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- setLower(double) - 类 中的方法org.apache.spark.ml.feature.RobustScaler
-
- setLowerBoundsOnCoefficients(Matrix) - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
Set the lower bounds on coefficients if fitting under bound constrained optimization.
- setLowerBoundsOnIntercepts(Vector) - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
Set the lower bounds on intercepts if fitting under bound constrained optimization.
- setMainClass(String) - 类 中的方法org.apache.spark.launcher.AbstractLauncher
-
Sets the application class name for Java/Scala applications.
- setMainClass(String) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
- setMapSideCombine(boolean) - 类 中的方法org.apache.spark.rdd.ShuffledRDD
-
Set mapSideCombine flag for RDD's shuffle.
- setMaster(String) - 类 中的方法org.apache.spark.launcher.AbstractLauncher
-
Set the Spark master for the application.
- setMaster(String) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
- setMaster(String) - 类 中的方法org.apache.spark.SparkConf
-
The master URL to connect to, such as "local" to run locally with one thread, "local[4]" to
run locally with 4 cores, or "spark://master:7077" to run on a Spark standalone cluster.
- setMax(double) - 类 中的方法org.apache.spark.ml.feature.MinMaxScaler
-
- setMax(double) - 类 中的方法org.apache.spark.ml.feature.MinMaxScalerModel
-
- setMaxBins(int) - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- setMaxBins(int) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- setMaxBins(int) - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- setMaxBins(int) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- setMaxBins(int) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- setMaxBins(int) - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- setMaxBins(int) - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- setMaxCategories(int) - 类 中的方法org.apache.spark.ml.feature.VectorIndexer
-
- setMaxDepth(int) - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- setMaxDepth(int) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- setMaxDepth(int) - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- setMaxDepth(int) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- setMaxDepth(int) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- setMaxDepth(int) - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- setMaxDepth(int) - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- setMaxDF(double) - 类 中的方法org.apache.spark.ml.feature.CountVectorizer
-
- setMaxIter(int) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- setMaxIter(int) - 类 中的方法org.apache.spark.ml.classification.LinearSVC
-
Set the maximum number of iterations.
- setMaxIter(int) - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
Set the maximum number of iterations.
- setMaxIter(int) - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
Set the maximum number of iterations.
- setMaxIter(int) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeans
-
- setMaxIter(int) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixture
-
- setMaxIter(int) - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- setMaxIter(int) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- setMaxIter(int) - 类 中的方法org.apache.spark.ml.clustering.PowerIterationClustering
-
- setMaxIter(int) - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- setMaxIter(int) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setMaxIter(int) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
Set the maximum number of iterations.
- setMaxIter(int) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- setMaxIter(int) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
Sets the maximum number of iterations (applicable for solver "irls").
- setMaxIter(int) - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
Set the maximum number of iterations.
- setMaxIterations(int) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeans
-
Sets the max number of k-means iterations to split clusters (default: 20).
- setMaxIterations(int) - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixture
-
Set the maximum number of iterations allowed.
- setMaxIterations(int) - 类 中的方法org.apache.spark.mllib.clustering.KMeans
-
Set maximum number of iterations allowed.
- setMaxIterations(int) - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Set the maximum number of iterations allowed.
- setMaxIterations(int) - 类 中的方法org.apache.spark.mllib.clustering.PowerIterationClustering
-
Set maximum number of iterations of the power iteration loop
- setMaxLocalProjDBSize(long) - 类 中的方法org.apache.spark.ml.fpm.PrefixSpan
-
- setMaxLocalProjDBSize(long) - 类 中的方法org.apache.spark.mllib.fpm.PrefixSpan
-
Sets the maximum number of items (including delimiters used in the internal storage format)
allowed in a projected database before local processing (default: 32000000L).
- setMaxMemoryInMB(int) - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- setMaxMemoryInMB(int) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- setMaxMemoryInMB(int) - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- setMaxMemoryInMB(int) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- setMaxMemoryInMB(int) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- setMaxMemoryInMB(int) - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- setMaxMemoryInMB(int) - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- setMaxPatternLength(int) - 类 中的方法org.apache.spark.ml.fpm.PrefixSpan
-
- setMaxPatternLength(int) - 类 中的方法org.apache.spark.mllib.fpm.PrefixSpan
-
Sets maximal pattern length (default: 10).
- setMaxSentenceLength(int) - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- setMaxSentenceLength(int) - 类 中的方法org.apache.spark.mllib.feature.Word2Vec
-
Sets the maximum length (in words) of each sentence in the input data.
- setMetricLabel(double) - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- setMetricLabel(double) - 类 中的方法org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator
-
- setMetricName(String) - 类 中的方法org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
-
- setMetricName(String) - 类 中的方法org.apache.spark.ml.evaluation.ClusteringEvaluator
-
- setMetricName(String) - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- setMetricName(String) - 类 中的方法org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator
-
- setMetricName(String) - 类 中的方法org.apache.spark.ml.evaluation.RankingEvaluator
-
- setMetricName(String) - 类 中的方法org.apache.spark.ml.evaluation.RegressionEvaluator
-
- setMin(double) - 类 中的方法org.apache.spark.ml.feature.MinMaxScaler
-
- setMin(double) - 类 中的方法org.apache.spark.ml.feature.MinMaxScalerModel
-
- setMinConfidence(double) - 类 中的方法org.apache.spark.ml.fpm.FPGrowth
-
- setMinConfidence(double) - 类 中的方法org.apache.spark.ml.fpm.FPGrowthModel
-
- setMinConfidence(double) - 类 中的方法org.apache.spark.mllib.fpm.AssociationRules
-
Sets the minimal confidence (default: 0.8).
- setMinCount(int) - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- setMinCount(int) - 类 中的方法org.apache.spark.mllib.feature.Word2Vec
-
Sets minCount, the minimum number of times a token must appear to be included in the word2vec
model's vocabulary (default: 5).
- setMinDF(double) - 类 中的方法org.apache.spark.ml.feature.CountVectorizer
-
- setMinDivisibleClusterSize(double) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeans
-
- setMinDivisibleClusterSize(double) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeans
-
Sets the minimum number of points (if greater than or equal to 1.0) or the minimum proportion
of points (if less than 1.0) of a divisible cluster (default: 1).
- setMinDocFreq(int) - 类 中的方法org.apache.spark.ml.feature.IDF
-
- setMiniBatchFraction(double) - 类 中的方法org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD
-
Set the fraction of each batch to use for updates.
- setMiniBatchFraction(double) - 类 中的方法org.apache.spark.mllib.clustering.OnlineLDAOptimizer
-
Mini-batch fraction in (0, 1], which sets the fraction of document sampled and used in
each iteration.
- setMiniBatchFraction(double) - 类 中的方法org.apache.spark.mllib.optimization.GradientDescent
-
Set fraction of data to be used for each SGD iteration.
- setMiniBatchFraction(double) - 类 中的方法org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
-
Set the fraction of each batch to use for updates.
- setMinInfoGain(double) - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- setMinInfoGain(double) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- setMinInfoGain(double) - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- setMinInfoGain(double) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- setMinInfoGain(double) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- setMinInfoGain(double) - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- setMinInfoGain(double) - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- setMinInstancesPerNode(int) - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- setMinInstancesPerNode(int) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- setMinInstancesPerNode(int) - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- setMinInstancesPerNode(int) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- setMinInstancesPerNode(int) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- setMinInstancesPerNode(int) - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- setMinInstancesPerNode(int) - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- setMinSupport(double) - 类 中的方法org.apache.spark.ml.fpm.FPGrowth
-
- setMinSupport(double) - 类 中的方法org.apache.spark.ml.fpm.PrefixSpan
-
- setMinSupport(double) - 类 中的方法org.apache.spark.mllib.fpm.FPGrowth
-
Sets the minimal support level (default: 0.3).
- setMinSupport(double) - 类 中的方法org.apache.spark.mllib.fpm.PrefixSpan
-
Sets the minimal support level (default: 0.1).
- setMinTF(double) - 类 中的方法org.apache.spark.ml.feature.CountVectorizer
-
- setMinTF(double) - 类 中的方法org.apache.spark.ml.feature.CountVectorizerModel
-
- setMinTokenLength(int) - 类 中的方法org.apache.spark.ml.feature.RegexTokenizer
-
- setMinWeightFractionPerNode(double) - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- setMinWeightFractionPerNode(double) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- setMinWeightFractionPerNode(double) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- setMinWeightFractionPerNode(double) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- setMinWeightFractionPerNode(double) - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- setMissingValue(double) - 类 中的方法org.apache.spark.ml.feature.Imputer
-
- setModelType(String) - 类 中的方法org.apache.spark.ml.classification.NaiveBayes
-
Set the model type using a string (case-sensitive).
- setModelType(String) - 类 中的方法org.apache.spark.mllib.classification.NaiveBayes
-
Set the model type using a string (case-sensitive).
- setN(int) - 类 中的方法org.apache.spark.ml.feature.NGram
-
- setName(String) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Assign a name to this RDD
- setName(String) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Assign a name to this RDD
- setName(String) - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Assign a name to this RDD
- setName(String) - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
- setName(String) - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- setName(String) - 类 中的方法org.apache.spark.rdd.RDD
-
Assign a name to this RDD
- setNames(String[]) - 类 中的方法org.apache.spark.ml.feature.VectorSlicer
-
- setNonnegative(boolean) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setNonnegative(boolean) - 类 中的方法org.apache.spark.mllib.recommendation.ALS
-
Set whether the least-squares problems solved at each iteration should have
nonnegativity constraints.
- setNullAt(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- setNullAt(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarRow
-
- setNumBins(int) - 类 中的方法org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
-
- setNumBlocks(int) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
Sets both numUserBlocks and numItemBlocks to the specific value.
- setNumBuckets(int) - 类 中的方法org.apache.spark.ml.feature.QuantileDiscretizer
-
- setNumBucketsArray(int[]) - 类 中的方法org.apache.spark.ml.feature.QuantileDiscretizer
-
- setNumClasses(int) - 类 中的方法org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
-
Set the number of possible outcomes for k classes classification problem in
Multinomial Logistic Regression.
- setNumClasses(int) - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- setNumCorrections(int) - 类 中的方法org.apache.spark.mllib.optimization.LBFGS
-
Set the number of corrections used in the LBFGS update.
- setNumFeatures(int) - 类 中的方法org.apache.spark.ml.feature.FeatureHasher
-
- setNumFeatures(int) - 类 中的方法org.apache.spark.ml.feature.HashingTF
-
- setNumFolds(int) - 类 中的方法org.apache.spark.ml.tuning.CrossValidator
-
- setNumHashTables(int) - 类 中的方法org.apache.spark.ml.feature.BucketedRandomProjectionLSH
-
- setNumHashTables(int) - 类 中的方法org.apache.spark.ml.feature.MinHashLSH
-
- setNumItemBlocks(int) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setNumIterations(int) - 类 中的方法org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD
-
Set the number of iterations of gradient descent to run per update.
- setNumIterations(int) - 类 中的方法org.apache.spark.mllib.feature.Word2Vec
-
Sets number of iterations (default: 1), which should be smaller than or equal to number of
partitions.
- setNumIterations(int) - 类 中的方法org.apache.spark.mllib.optimization.GradientDescent
-
Set the number of iterations for SGD.
- setNumIterations(int) - 类 中的方法org.apache.spark.mllib.optimization.LBFGS
-
Set the maximal number of iterations for L-BFGS.
- setNumIterations(int) - 类 中的方法org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
-
Set the number of iterations of gradient descent to run per update.
- setNumIterations(int) - 类 中的方法org.apache.spark.mllib.tree.configuration.BoostingStrategy
-
- setNumPartitions(int) - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- setNumPartitions(int) - 类 中的方法org.apache.spark.ml.fpm.FPGrowth
-
- setNumPartitions(int) - 类 中的方法org.apache.spark.mllib.feature.Word2Vec
-
Sets number of partitions (default: 1).
- setNumPartitions(int) - 类 中的方法org.apache.spark.mllib.fpm.FPGrowth
-
Sets the number of partitions used by parallel FP-growth (default: same as input data).
- setNumRows(int) - 类 中的方法org.apache.spark.sql.vectorized.ColumnarBatch
-
Sets the number of rows in this batch.
- setNumTopFeatures(int) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- setNumTopFeatures(int) - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelector
-
- setNumTrees(int) - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- setNumTrees(int) - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- setNumUserBlocks(int) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setOffsetCol(String) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
Sets the value of param offsetCol.
- setOptimizeDocConcentration(boolean) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- setOptimizeDocConcentration(boolean) - 类 中的方法org.apache.spark.mllib.clustering.OnlineLDAOptimizer
-
Sets whether to optimize docConcentration parameter during training.
- setOptimizer(String) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- setOptimizer(LDAOptimizer) - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
:: DeveloperApi ::
LDAOptimizer used to perform the actual calculation (default = EMLDAOptimizer)
- setOptimizer(String) - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Set the LDAOptimizer used to perform the actual calculation by algorithm name.
- setOrNull(long, int, int) - 类 中的方法org.apache.spark.sql.types.Decimal
-
Set this Decimal to the given unscaled Long, with a given precision and scale,
and return it, or return null if it cannot be set due to overflow.
- setOut(PrintStream) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.Binarizer
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.BucketedRandomProjectionLSH
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.BucketedRandomProjectionLSHModel
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.Bucketizer
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelectorModel
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.CountVectorizer
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.CountVectorizerModel
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.FeatureHasher
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.HashingTF
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.IDF
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.IDFModel
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.Imputer
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.ImputerModel
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.IndexToString
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.Interaction
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.MaxAbsScaler
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.MaxAbsScalerModel
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.MinHashLSH
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.MinHashLSHModel
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.MinMaxScaler
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.MinMaxScalerModel
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.OneHotEncoder
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.PCA
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.PCAModel
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.QuantileDiscretizer
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.RobustScaler
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.RobustScalerModel
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.StandardScaler
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.StandardScalerModel
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.StopWordsRemover
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.StringIndexer
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.StringIndexerModel
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.VectorAssembler
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.VectorIndexer
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.VectorIndexerModel
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.VectorSlicer
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.feature.Word2VecModel
-
- setOutputCol(String) - 类 中的方法org.apache.spark.ml.UnaryTransformer
-
- setOutputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.Binarizer
-
- setOutputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.Bucketizer
-
- setOutputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.Imputer
-
- setOutputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.ImputerModel
-
- setOutputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.OneHotEncoder
-
- setOutputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.OneHotEncoderModel
-
- setOutputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.QuantileDiscretizer
-
- setOutputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.StringIndexer
-
- setOutputCols(String[]) - 类 中的方法org.apache.spark.ml.feature.StringIndexerModel
-
- setP(double) - 类 中的方法org.apache.spark.ml.feature.Normalizer
-
- setParallelism(int) - 类 中的方法org.apache.spark.ml.classification.OneVsRest
-
The implementation of parallel one vs. rest runs the classification for
each class in a separate threads.
- setParallelism(int) - 类 中的方法org.apache.spark.ml.tuning.CrossValidator
-
Set the maximum level of parallelism to evaluate models in parallel.
- setParallelism(int) - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplit
-
Set the maximum level of parallelism to evaluate models in parallel.
- setParent(Estimator<M>) - 类 中的方法org.apache.spark.ml.Model
-
Sets the parent of this model (Java API).
- setPattern(String) - 类 中的方法org.apache.spark.ml.feature.RegexTokenizer
-
- setPeacePeriod(int) - 类 中的方法org.apache.spark.mllib.stat.test.StreamingTest
-
Set the number of initial batches to ignore.
- setPercentile(double) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- setPercentile(double) - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelector
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.classification.OneVsRest
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.classification.OneVsRestModel
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeans
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixture
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.clustering.KMeansModel
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.evaluation.ClusteringEvaluator
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.evaluation.RankingEvaluator
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.evaluation.RegressionEvaluator
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.fpm.FPGrowth
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.fpm.FPGrowthModel
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.PredictionModel
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.Predictor
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.recommendation.ALSModel
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.regression.IsotonicRegression
-
- setPredictionCol(String) - 类 中的方法org.apache.spark.ml.regression.IsotonicRegressionModel
-
- setProbabilityCol(String) - 类 中的方法org.apache.spark.ml.classification.ProbabilisticClassificationModel
-
- setProbabilityCol(String) - 类 中的方法org.apache.spark.ml.classification.ProbabilisticClassifier
-
- setProbabilityCol(String) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixture
-
- setProbabilityCol(String) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- setProbabilityCol(String) - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- setProductBlocks(int) - 类 中的方法org.apache.spark.mllib.recommendation.ALS
-
Set the number of product blocks to parallelize the computation.
- setPropertiesFile(String) - 类 中的方法org.apache.spark.launcher.AbstractLauncher
-
Set a custom properties file with Spark configuration for the application.
- setPropertiesFile(String) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
- setProperty(String, String) - 接口 中的静态方法org.apache.spark.sql.connector.catalog.NamespaceChange
-
Create a NamespaceChange for setting a namespace property.
- setProperty(String, String) - 接口 中的静态方法org.apache.spark.sql.connector.catalog.TableChange
-
Create a TableChange for setting a table property.
- setQuantileCalculationStrategy(Enumeration.Value) - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- setQuantileProbabilities(double[]) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- setQuantileProbabilities(double[]) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- setQuantilesCol(String) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- setQuantilesCol(String) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- setRandomCenters(int, double, long) - 类 中的方法org.apache.spark.mllib.clustering.StreamingKMeans
-
Initialize random centers, requiring only the number of dimensions.
- setRank(int) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setRank(int) - 类 中的方法org.apache.spark.mllib.recommendation.ALS
-
Set the rank of the feature matrices computed (number of features).
- setRatingCol(String) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setRawPredictionCol(String) - 类 中的方法org.apache.spark.ml.classification.ClassificationModel
-
- setRawPredictionCol(String) - 类 中的方法org.apache.spark.ml.classification.Classifier
-
- setRawPredictionCol(String) - 类 中的方法org.apache.spark.ml.classification.OneVsRest
-
- setRawPredictionCol(String) - 类 中的方法org.apache.spark.ml.classification.OneVsRestModel
-
- setRawPredictionCol(String) - 类 中的方法org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
-
- setRegParam(double) - 类 中的方法org.apache.spark.ml.classification.LinearSVC
-
Set the regularization parameter.
- setRegParam(double) - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
Set the regularization parameter.
- setRegParam(double) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setRegParam(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
Sets the regularization parameter for L2 regularization.
- setRegParam(double) - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
Set the regularization parameter.
- setRegParam(double) - 类 中的方法org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD
-
Set the regularization parameter.
- setRegParam(double) - 类 中的方法org.apache.spark.mllib.optimization.GradientDescent
-
Set the regularization parameter.
- setRegParam(double) - 类 中的方法org.apache.spark.mllib.optimization.LBFGS
-
Set the regularization parameter.
- setRegParam(double) - 类 中的方法org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
-
Set the regularization parameter.
- setRelativeError(double) - 类 中的方法org.apache.spark.ml.feature.Imputer
-
- setRelativeError(double) - 类 中的方法org.apache.spark.ml.feature.QuantileDiscretizer
-
- setRelativeError(double) - 类 中的方法org.apache.spark.ml.feature.RobustScaler
-
- setRequiredColumns(Configuration, StructType, StructType) - 类 中的静态方法org.apache.spark.sql.hive.orc.OrcFileFormat
-
- setRest(long, int, VD, ED) - 类 中的方法org.apache.spark.graphx.impl.AggregatingEdgeContext
-
- setSample(RDD<Object>) - 类 中的方法org.apache.spark.mllib.stat.KernelDensity
-
Sets the sample to use for density estimation.
- setSample(JavaRDD<Double>) - 类 中的方法org.apache.spark.mllib.stat.KernelDensity
-
Sets the sample to use for density estimation (for Java users).
- setScalingVec(Vector) - 类 中的方法org.apache.spark.ml.feature.ElementwiseProduct
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
Set the seed for weights initialization if weights are not set
- setSeed(long) - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeans
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixture
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.feature.BucketedRandomProjectionLSH
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.feature.MinHashLSH
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.tuning.CrossValidator
-
- setSeed(long) - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplit
-
- setSeed(long) - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeans
-
Sets the random seed (default: hash value of the class name).
- setSeed(long) - 类 中的方法org.apache.spark.mllib.clustering.GaussianMixture
-
Set the random seed
- setSeed(long) - 类 中的方法org.apache.spark.mllib.clustering.KMeans
-
Set the random seed for cluster initialization.
- setSeed(long) - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Set the random seed for cluster initialization.
- setSeed(long) - 类 中的方法org.apache.spark.mllib.clustering.LocalLDAModel
-
Set the random seed for cluster initialization.
- setSeed(long) - 类 中的方法org.apache.spark.mllib.feature.Word2Vec
-
Sets random seed (default: a random long integer).
- setSeed(long) - 类 中的方法org.apache.spark.mllib.random.ExponentialGenerator
-
- setSeed(long) - 类 中的方法org.apache.spark.mllib.random.GammaGenerator
-
- setSeed(long) - 类 中的方法org.apache.spark.mllib.random.LogNormalGenerator
-
- setSeed(long) - 类 中的方法org.apache.spark.mllib.random.PoissonGenerator
-
- setSeed(long) - 类 中的方法org.apache.spark.mllib.random.StandardNormalGenerator
-
- setSeed(long) - 类 中的方法org.apache.spark.mllib.random.UniformGenerator
-
- setSeed(long) - 类 中的方法org.apache.spark.mllib.random.WeibullGenerator
-
- setSeed(long) - 类 中的方法org.apache.spark.mllib.recommendation.ALS
-
Sets a random seed to have deterministic results.
- setSeed(long) - 类 中的方法org.apache.spark.util.random.BernoulliCellSampler
-
- setSeed(long) - 类 中的方法org.apache.spark.util.random.BernoulliSampler
-
- setSeed(long) - 类 中的方法org.apache.spark.util.random.PoissonSampler
-
- setSeed(long) - 接口 中的方法org.apache.spark.util.random.Pseudorandom
-
Set random seed.
- setSelectorType(String) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- setSelectorType(String) - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelector
-
- setSequenceCol(String) - 类 中的方法org.apache.spark.ml.fpm.PrefixSpan
-
- setSerializer(Serializer) - 类 中的方法org.apache.spark.rdd.CoGroupedRDD
-
Set a serializer for this RDD's shuffle, or null to use the default (spark.serializer)
- setSerializer(Serializer) - 类 中的方法org.apache.spark.rdd.ShuffledRDD
-
Set a serializer for this RDD's shuffle, or null to use the default (spark.serializer)
- setSize(int) - 类 中的方法org.apache.spark.ml.feature.VectorSizeHint
-
- setSmoothing(double) - 类 中的方法org.apache.spark.ml.classification.NaiveBayes
-
Set the smoothing parameter.
- setSolver(String) - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
Sets the value of param solver.
- setSolver(String) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
Sets the solver algorithm used for optimization.
- setSolver(String) - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
Set the solver algorithm used for optimization.
- setSparkContextSessionConf(SparkSession, Map<Object, Object>) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
- setSparkHome(String) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
Set a custom Spark installation location for the application.
- setSparkHome(String) - 类 中的方法org.apache.spark.SparkConf
-
Set the location where Spark is installed on worker nodes.
- setSplits(double[]) - 类 中的方法org.apache.spark.ml.feature.Bucketizer
-
- setSplitsArray(double[][]) - 类 中的方法org.apache.spark.ml.feature.Bucketizer
-
- setSQLReadObject(Function2<DataInputStream, Object, Object>) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- setSQLWriteObject(Function2<DataOutputStream, Object, Object>) - 类 中的静态方法org.apache.spark.api.r.SerDe
-
- setSrcCol(String) - 类 中的方法org.apache.spark.ml.clustering.PowerIterationClustering
-
- setSrcOnly(long, int, VD) - 类 中的方法org.apache.spark.graphx.impl.AggregatingEdgeContext
-
- setStages(PipelineStage[]) - 类 中的方法org.apache.spark.ml.Pipeline
-
- setStandardization(boolean) - 类 中的方法org.apache.spark.ml.classification.LinearSVC
-
Whether to standardize the training features before fitting the model.
- setStandardization(boolean) - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
Whether to standardize the training features before fitting the model.
- setStandardization(boolean) - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
Whether to standardize the training features before fitting the model.
- setStatement(String) - 类 中的方法org.apache.spark.ml.feature.SQLTransformer
-
- setStepSize(double) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- setStepSize(double) - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
Sets the value of param stepSize (applicable only for solver "gd").
- setStepSize(double) - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- setStepSize(double) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- setStepSize(double) - 类 中的方法org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD
-
Set the step size for gradient descent.
- setStepSize(double) - 类 中的方法org.apache.spark.mllib.optimization.GradientDescent
-
Set the initial step size of SGD for the first step.
- setStepSize(double) - 类 中的方法org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
-
Set the step size for gradient descent.
- setStopWords(String[]) - 类 中的方法org.apache.spark.ml.feature.StopWordsRemover
-
- setStrategy(String) - 类 中的方法org.apache.spark.ml.feature.Imputer
-
Imputation strategy.
- setStringIndexerOrderType(String) - 类 中的方法org.apache.spark.ml.feature.RFormula
-
- setStringOrderType(String) - 类 中的方法org.apache.spark.ml.feature.StringIndexer
-
- setSubsamplingRate(double) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- setSubsamplingRate(double) - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- setSubsamplingRate(double) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- setSubsamplingRate(double) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- setSubsamplingRate(double) - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- setSubsamplingRate(double) - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- setSummary(Option<T>) - 接口 中的方法org.apache.spark.ml.util.HasTrainingSummary
-
- setTau0(double) - 类 中的方法org.apache.spark.mllib.clustering.OnlineLDAOptimizer
-
A (positive) learning parameter that downweights early iterations.
- setTestMethod(String) - 类 中的方法org.apache.spark.mllib.stat.test.StreamingTest
-
Set the statistical method used for significance testing.
- setThreshold(double) - 类 中的方法org.apache.spark.ml.classification.LinearSVC
-
Set threshold in binary classification.
- setThreshold(double) - 类 中的方法org.apache.spark.ml.classification.LinearSVCModel
-
- setThreshold(double) - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
- setThreshold(double) - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- setThreshold(double) - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionParams
-
Set threshold in binary classification, in range [0, 1].
- setThreshold(double) - 类 中的方法org.apache.spark.ml.feature.Binarizer
-
- setThreshold(double) - 类 中的方法org.apache.spark.mllib.classification.LogisticRegressionModel
-
Sets the threshold that separates positive predictions from negative predictions
in Binary Logistic Regression.
- setThreshold(double) - 类 中的方法org.apache.spark.mllib.classification.SVMModel
-
Sets the threshold that separates positive predictions from negative predictions.
- setThresholds(double[]) - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
- setThresholds(double[]) - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- setThresholds(double[]) - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionParams
-
Set thresholds in multiclass (or binary) classification to adjust the probability of
predicting each class.
- setThresholds(double[]) - 类 中的方法org.apache.spark.ml.classification.ProbabilisticClassificationModel
-
- setThresholds(double[]) - 类 中的方法org.apache.spark.ml.classification.ProbabilisticClassifier
-
- setThresholds(double[]) - 类 中的方法org.apache.spark.ml.feature.Binarizer
-
- setThroughOrigin(boolean) - 类 中的方法org.apache.spark.ml.evaluation.RegressionEvaluator
-
- setTimeoutDuration(long) - 接口 中的方法org.apache.spark.sql.streaming.GroupState
-
Set the timeout duration in ms for this key.
- setTimeoutDuration(String) - 接口 中的方法org.apache.spark.sql.streaming.GroupState
-
Set the timeout duration for this key as a string.
- setTimeoutTimestamp(long) - 接口 中的方法org.apache.spark.sql.streaming.GroupState
-
Set the timeout timestamp for this key as milliseconds in epoch time.
- setTimeoutTimestamp(long, String) - 接口 中的方法org.apache.spark.sql.streaming.GroupState
-
Set the timeout timestamp for this key as milliseconds in epoch time and an additional
duration as a string (e.g. "1 hour", "2 days", etc.).
- setTimeoutTimestamp(Date) - 接口 中的方法org.apache.spark.sql.streaming.GroupState
-
Set the timeout timestamp for this key as a java.sql.Date.
- setTimeoutTimestamp(Date, String) - 接口 中的方法org.apache.spark.sql.streaming.GroupState
-
Set the timeout timestamp for this key as a java.sql.Date and an additional
duration as a string (e.g. "1 hour", "2 days", etc.).
- setTol(double) - 类 中的方法org.apache.spark.ml.classification.LinearSVC
-
Set the convergence tolerance of iterations.
- setTol(double) - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
Set the convergence tolerance of iterations.
- setTol(double) - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
Set the convergence tolerance of iterations.
- setTol(double) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixture
-
- setTol(double) - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- setTol(double) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
Set the convergence tolerance of iterations.
- setTol(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
Sets the convergence tolerance of iterations.
- setTol(double) - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
Set the convergence tolerance of iterations.
- setToLowercase(boolean) - 类 中的方法org.apache.spark.ml.feature.RegexTokenizer
-
- setTopicConcentration(double) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- setTopicConcentration(double) - 类 中的方法org.apache.spark.mllib.clustering.LDA
-
Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics'
distributions over terms.
- setTopicDistributionCol(String) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- setTopicDistributionCol(String) - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
- setTrainRatio(double) - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplit
-
- setTreeStrategy(Strategy) - 类 中的方法org.apache.spark.mllib.tree.configuration.BoostingStrategy
-
- setUiRoot(ContextHandler, UIRoot) - 类 中的静态方法org.apache.spark.status.api.v1.UIRootFromServletContext
-
- setupCommitter(TaskAttemptContext) - 类 中的方法org.apache.spark.internal.io.HadoopMapRedCommitProtocol
-
- setUpdater(Updater) - 类 中的方法org.apache.spark.mllib.optimization.GradientDescent
-
Set the updater function to actually perform a gradient step in a given direction.
- setUpdater(Updater) - 类 中的方法org.apache.spark.mllib.optimization.LBFGS
-
Set the updater function to actually perform a gradient step in a given direction.
- SetupDriver(org.apache.spark.rpc.RpcEndpointRef) - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.SetupDriver
-
- SetupDriver$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.SetupDriver$
-
- setupGroups(int, org.apache.spark.rdd.DefaultPartitionCoalescer.PartitionLocations) - 类 中的方法org.apache.spark.rdd.DefaultPartitionCoalescer
-
Initializes targetLen partition groups.
- setupJob(JobContext) - 类 中的方法org.apache.spark.internal.io.FileCommitProtocol
-
Setups up a job.
- setupJob(JobContext) - 类 中的方法org.apache.spark.internal.io.HadoopMapReduceCommitProtocol
-
- setUpper(double) - 类 中的方法org.apache.spark.ml.feature.RobustScaler
-
- setUpperBoundsOnCoefficients(Matrix) - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
Set the upper bounds on coefficients if fitting under bound constrained optimization.
- setUpperBoundsOnIntercepts(Vector) - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
Set the upper bounds on intercepts if fitting under bound constrained optimization.
- setupTask(TaskAttemptContext) - 类 中的方法org.apache.spark.internal.io.FileCommitProtocol
-
Sets up a task within a job.
- setupTask(TaskAttemptContext) - 类 中的方法org.apache.spark.internal.io.HadoopMapReduceCommitProtocol
-
- setupUI(org.apache.spark.ui.SparkUI) - 接口 中的方法org.apache.spark.status.AppHistoryServerPlugin
-
Sets up UI of this plugin to rebuild the history UI.
- setUseNodeIdCache(boolean) - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- setUserBlocks(int) - 类 中的方法org.apache.spark.mllib.recommendation.ALS
-
Set the number of user blocks to parallelize the computation.
- setUserCol(String) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- setUserCol(String) - 类 中的方法org.apache.spark.ml.recommendation.ALSModel
-
- setValidateData(boolean) - 类 中的方法org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm
-
Set if the algorithm should validate data before training.
- setValidationIndicatorCol(String) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- setValidationIndicatorCol(String) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- setValidationTol(double) - 类 中的方法org.apache.spark.mllib.tree.configuration.BoostingStrategy
-
- setVarianceCol(String) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- setVarianceCol(String) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
- setVariancePower(double) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
Sets the value of param variancePower.
- setVectorSize(int) - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- setVectorSize(int) - 类 中的方法org.apache.spark.mllib.feature.Word2Vec
-
Sets vector size (default: 100).
- setVerbose(boolean) - 类 中的方法org.apache.spark.launcher.AbstractLauncher
-
Enables verbose reporting for SparkSubmit.
- setVerbose(boolean) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
- setVocabSize(int) - 类 中的方法org.apache.spark.ml.feature.CountVectorizer
-
- setWeightCol(String) - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
Sets the value of param weightCol.
- setWeightCol(String) - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
Sets the value of param weightCol.
- setWeightCol(String) - 类 中的方法org.apache.spark.ml.classification.LinearSVC
-
Set the value of param weightCol.
- setWeightCol(String) - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
Sets the value of param weightCol.
- setWeightCol(String) - 类 中的方法org.apache.spark.ml.classification.NaiveBayes
-
Sets the value of param weightCol.
- setWeightCol(String) - 类 中的方法org.apache.spark.ml.classification.OneVsRest
-
Sets the value of param weightCol.
- setWeightCol(String) - 类 中的方法org.apache.spark.ml.clustering.PowerIterationClustering
-
- setWeightCol(String) - 类 中的方法org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
-
- setWeightCol(String) - 类 中的方法org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-
- setWeightCol(String) - 类 中的方法org.apache.spark.ml.evaluation.RegressionEvaluator
-
- setWeightCol(String) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
Sets the value of param weightCol.
- setWeightCol(String) - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
Sets the value of param weightCol.
- setWeightCol(String) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
Sets the value of param weightCol.
- setWeightCol(String) - 类 中的方法org.apache.spark.ml.regression.IsotonicRegression
-
- setWeightCol(String) - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
Whether to over-/under-sample training instances according to the given weights in weightCol.
- setWindowSize(int) - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- setWindowSize(int) - 类 中的方法org.apache.spark.mllib.feature.Word2Vec
-
Sets the window of words (default: 5)
- setWindowSize(int) - 类 中的方法org.apache.spark.mllib.stat.test.StreamingTest
-
Set the number of batches to compute significance tests over.
- setWithCentering(boolean) - 类 中的方法org.apache.spark.ml.feature.RobustScaler
-
- setWithMean(boolean) - 类 中的方法org.apache.spark.ml.feature.StandardScaler
-
- setWithMean(boolean) - 类 中的方法org.apache.spark.mllib.feature.StandardScalerModel
-
:: DeveloperApi ::
- setWithScaling(boolean) - 类 中的方法org.apache.spark.ml.feature.RobustScaler
-
- setWithStd(boolean) - 类 中的方法org.apache.spark.ml.feature.StandardScaler
-
- setWithStd(boolean) - 类 中的方法org.apache.spark.mllib.feature.StandardScalerModel
-
:: DeveloperApi ::
- sha1(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Calculates the SHA-1 digest of a binary column and returns the value
as a 40 character hex string.
- sha2(Column, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Calculates the SHA-2 family of hash functions of a binary column and
returns the value as a hex string.
- shape() - 类 中的方法org.apache.spark.mllib.random.GammaGenerator
-
- SharedMessageLoop - org.apache.spark.rpc.netty中的类
-
A message loop that serves multiple RPC endpoints, using a shared thread pool.
- SharedMessageLoop(SparkConf, Dispatcher, int) - 类 的构造器org.apache.spark.rpc.netty.SharedMessageLoop
-
- SharedParamsCodeGen - org.apache.spark.ml.param.shared中的类
-
Code generator for shared params (sharedParams.scala).
- SharedParamsCodeGen() - 类 的构造器org.apache.spark.ml.param.shared.SharedParamsCodeGen
-
- SharedReadWrite$() - 类 的构造器org.apache.spark.ml.Pipeline.SharedReadWrite$
-
- sharedState() - 类 中的方法org.apache.spark.sql.SparkSession
-
- shiftLeft(Column, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Shift the given value numBits left.
- shiftRight(Column, int) - 类 中的静态方法org.apache.spark.sql.functions
-
(Signed) shift the given value numBits right.
- shiftRightUnsigned(Column, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Unsigned shift the given value numBits right.
- SHORT() - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for nullable short type.
- ShortestPaths - org.apache.spark.graphx.lib中的类
-
Computes shortest paths to the given set of landmark vertices, returning a graph where each
vertex attribute is a map containing the shortest-path distance to each reachable landmark.
- ShortestPaths() - 类 的构造器org.apache.spark.graphx.lib.ShortestPaths
-
- ShortExactNumeric - org.apache.spark.sql.types中的类
-
- ShortExactNumeric() - 类 的构造器org.apache.spark.sql.types.ShortExactNumeric
-
- shortName() - 接口 中的方法org.apache.spark.ml.util.MLFormatRegister
-
- shortName() - 类 中的方法org.apache.spark.sql.hive.execution.HiveFileFormat
-
- shortName() - 类 中的方法org.apache.spark.sql.hive.orc.OrcFileFormat
-
- shortName() - 接口 中的方法org.apache.spark.sql.sources.DataSourceRegister
-
The string that represents the format that this data source provider uses.
- shortTimeUnitString(TimeUnit) - 类 中的静态方法org.apache.spark.streaming.ui.UIUtils
-
Return the short string for a TimeUnit.
- ShortType - 类 中的静态变量org.apache.spark.sql.types.DataTypes
-
Gets the ShortType object.
- ShortType - org.apache.spark.sql.types中的类
-
The data type representing Short values.
- ShortType() - 类 的构造器org.apache.spark.sql.types.ShortType
-
- shortVersion(String) - 类 中的静态方法org.apache.spark.util.VersionUtils
-
Given a Spark version string, return the short version string.
- shouldCloseFileAfterWrite(SparkConf, boolean) - 类 中的静态方法org.apache.spark.streaming.util.WriteAheadLogUtils
-
- shouldDistributeGaussians(int, int) - 类 中的静态方法org.apache.spark.mllib.clustering.GaussianMixture
-
Heuristic to distribute the computation of the MultivariateGaussians, approximately when
d is greater than 25 except for when k is very small.
- shouldGoLeft(Vector) - 接口 中的方法org.apache.spark.ml.tree.Split
-
Return true (split to left) or false (split to right).
- shouldGoLeft(int, Split[]) - 接口 中的方法org.apache.spark.ml.tree.Split
-
Return true (split to left) or false (split to right).
- shouldOwn(Param<?>) - 接口 中的方法org.apache.spark.ml.param.Params
-
Validates that the input param belongs to this instance.
- shouldRollover(long) - 接口 中的方法org.apache.spark.util.logging.RollingPolicy
-
Whether rollover should be initiated at this moment
- show(int) - 类 中的方法org.apache.spark.sql.Dataset
-
Displays the Dataset in a tabular form.
- show() - 类 中的方法org.apache.spark.sql.Dataset
-
Displays the top 20 rows of Dataset in a tabular form.
- show(boolean) - 类 中的方法org.apache.spark.sql.Dataset
-
Displays the top 20 rows of Dataset in a tabular form.
- show(int, boolean) - 类 中的方法org.apache.spark.sql.Dataset
-
Displays the Dataset in a tabular form.
- show(int, int) - 类 中的方法org.apache.spark.sql.Dataset
-
Displays the Dataset in a tabular form.
- show(int, int, boolean) - 类 中的方法org.apache.spark.sql.Dataset
-
Displays the Dataset in a tabular form.
- showBytesDistribution(String, Function2<TaskInfo, TaskMetrics, Object>, Seq<Tuple2<TaskInfo, TaskMetrics>>) - 类 中的静态方法org.apache.spark.scheduler.StatsReportListener
-
- showBytesDistribution(String, Option<org.apache.spark.util.Distribution>) - 类 中的静态方法org.apache.spark.scheduler.StatsReportListener
-
- showBytesDistribution(String, org.apache.spark.util.Distribution) - 类 中的静态方法org.apache.spark.scheduler.StatsReportListener
-
- showDagVizForJob(int, Seq<org.apache.spark.ui.scope.RDDOperationGraph>) - 类 中的静态方法org.apache.spark.ui.UIUtils
-
Return a "DAG visualization" DOM element that expands into a visualization for a job.
- showDagVizForStage(int, Option<org.apache.spark.ui.scope.RDDOperationGraph>) - 类 中的静态方法org.apache.spark.ui.UIUtils
-
Return a "DAG visualization" DOM element that expands into a visualization for a stage.
- showDistribution(String, org.apache.spark.util.Distribution, Function1<Object, String>) - 类 中的静态方法org.apache.spark.scheduler.StatsReportListener
-
- showDistribution(String, Option<org.apache.spark.util.Distribution>, Function1<Object, String>) - 类 中的静态方法org.apache.spark.scheduler.StatsReportListener
-
- showDistribution(String, Option<org.apache.spark.util.Distribution>, String) - 类 中的静态方法org.apache.spark.scheduler.StatsReportListener
-
- showDistribution(String, String, Function2<TaskInfo, TaskMetrics, Object>, Seq<Tuple2<TaskInfo, TaskMetrics>>) - 类 中的静态方法org.apache.spark.scheduler.StatsReportListener
-
- showMillisDistribution(String, Option<org.apache.spark.util.Distribution>) - 类 中的静态方法org.apache.spark.scheduler.StatsReportListener
-
- showMillisDistribution(String, Function2<TaskInfo, TaskMetrics, Object>, Seq<Tuple2<TaskInfo, TaskMetrics>>) - 类 中的静态方法org.apache.spark.scheduler.StatsReportListener
-
- showMillisDistribution(String, Function1<BatchInfo, Option<Object>>) - 类 中的方法org.apache.spark.streaming.scheduler.StatsReportListener
-
- shuffle(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns a random permutation of the given array.
- SHUFFLE() - 类 中的静态方法org.apache.spark.storage.BlockId
-
- SHUFFLE_BATCH() - 类 中的静态方法org.apache.spark.storage.BlockId
-
- SHUFFLE_DATA() - 类 中的静态方法org.apache.spark.storage.BlockId
-
- SHUFFLE_INDEX() - 类 中的静态方法org.apache.spark.storage.BlockId
-
- SHUFFLE_LOCAL_BLOCKS() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- SHUFFLE_READ() - 类 中的静态方法org.apache.spark.ui.ToolTips
-
- SHUFFLE_READ_BLOCKED_TIME() - 类 中的静态方法org.apache.spark.ui.jobs.TaskDetailsClassNames
-
- SHUFFLE_READ_BLOCKED_TIME() - 类 中的静态方法org.apache.spark.ui.ToolTips
-
- SHUFFLE_READ_METRICS_PREFIX() - 类 中的静态方法org.apache.spark.InternalAccumulator
-
- SHUFFLE_READ_RECORDS() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- SHUFFLE_READ_REMOTE_SIZE() - 类 中的静态方法org.apache.spark.ui.jobs.TaskDetailsClassNames
-
- SHUFFLE_READ_REMOTE_SIZE() - 类 中的静态方法org.apache.spark.ui.ToolTips
-
- SHUFFLE_READ_TIME() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- SHUFFLE_REMOTE_BLOCKS() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- SHUFFLE_REMOTE_READS() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- SHUFFLE_REMOTE_READS_TO_DISK() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- SHUFFLE_SERVICE() - 类 中的静态方法org.apache.spark.metrics.MetricsSystemInstances
-
- SHUFFLE_TOTAL_BLOCKS() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- SHUFFLE_TOTAL_READS() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- SHUFFLE_WRITE() - 类 中的静态方法org.apache.spark.ui.ToolTips
-
- SHUFFLE_WRITE_METRICS_PREFIX() - 类 中的静态方法org.apache.spark.InternalAccumulator
-
- SHUFFLE_WRITE_RECORDS() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- SHUFFLE_WRITE_SIZE() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- SHUFFLE_WRITE_TIME() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- ShuffleBlockBatchId - org.apache.spark.storage中的类
-
- ShuffleBlockBatchId(int, long, int, int) - 类 的构造器org.apache.spark.storage.ShuffleBlockBatchId
-
- ShuffleBlockId - org.apache.spark.storage中的类
-
- ShuffleBlockId(int, long, int) - 类 的构造器org.apache.spark.storage.ShuffleBlockId
-
- shuffleCleaned(int) - 接口 中的方法org.apache.spark.CleanerListener
-
- ShuffleDataBlockId - org.apache.spark.storage中的类
-
- ShuffleDataBlockId(int, long, int) - 类 的构造器org.apache.spark.storage.ShuffleDataBlockId
-
- ShuffleDataIO - org.apache.spark.shuffle.api中的接口
-
:: Private ::
An interface for plugging in modules for storing and reading temporary shuffle data.
- ShuffleDependency<K,V,C> - org.apache.spark中的类
-
:: DeveloperApi ::
Represents a dependency on the output of a shuffle stage.
- ShuffleDependency(RDD<? extends Product2<K, V>>, Partitioner, Serializer, Option<Ordering<K>>, Option<Aggregator<K, V, C>>, boolean, ShuffleWriteProcessor, ClassTag<K>, ClassTag<V>, ClassTag<C>) - 类 的构造器org.apache.spark.ShuffleDependency
-
- ShuffledRDD<K,V,C> - org.apache.spark.rdd中的类
-
:: DeveloperApi ::
The resulting RDD from a shuffle (e.g. repartitioning of data).
- ShuffledRDD(RDD<? extends Product2<K, V>>, Partitioner, ClassTag<K>, ClassTag<V>, ClassTag<C>) - 类 的构造器org.apache.spark.rdd.ShuffledRDD
-
- ShuffleDriverComponents - org.apache.spark.shuffle.api中的接口
-
:: Private ::
An interface for building shuffle support modules for the Driver.
- ShuffleExecutorComponents - org.apache.spark.shuffle.api中的接口
-
:: Private ::
An interface for building shuffle support for Executors.
- ShuffleFetchCompletionListener - org.apache.spark.storage中的类
-
A listener to be called at the completion of the ShuffleBlockFetcherIterator
param: data the ShuffleBlockFetcherIterator to process
- ShuffleFetchCompletionListener(ShuffleBlockFetcherIterator) - 类 的构造器org.apache.spark.storage.ShuffleFetchCompletionListener
-
- shuffleFetchWaitTime() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- shuffleHandle() - 类 中的方法org.apache.spark.ShuffleDependency
-
- shuffleId() - 类 中的方法org.apache.spark.CleanShuffle
-
- shuffleId() - 类 中的方法org.apache.spark.FetchFailed
-
- shuffleId() - 类 中的方法org.apache.spark.ShuffleDependency
-
- shuffleId() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.RemoveShuffle
-
- shuffleId() - 类 中的方法org.apache.spark.storage.ShuffleBlockBatchId
-
- shuffleId() - 类 中的方法org.apache.spark.storage.ShuffleBlockId
-
- shuffleId() - 类 中的方法org.apache.spark.storage.ShuffleDataBlockId
-
- shuffleId() - 类 中的方法org.apache.spark.storage.ShuffleIndexBlockId
-
- ShuffleIndexBlockId - org.apache.spark.storage中的类
-
- ShuffleIndexBlockId(int, long, int) - 类 的构造器org.apache.spark.storage.ShuffleIndexBlockId
-
- shuffleLocalBlocksFetched() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- shuffleLocalBytesRead() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- shuffleManager() - 类 中的方法org.apache.spark.SparkEnv
-
- ShuffleMapOutputWriter - org.apache.spark.shuffle.api中的接口
-
:: Private ::
A top-level writer that returns child writers for persisting the output of a map task,
and then commits all of the writes as one atomic operation.
- ShufflePartitionWriter - org.apache.spark.shuffle.api中的接口
-
:: Private ::
An interface for opening streams to persist partition bytes to a backing data store.
- shuffleRead() - 类 中的方法org.apache.spark.status.api.v1.ExecutorStageSummary
-
- shuffleRead$() - 类 的构造器org.apache.spark.InternalAccumulator.shuffleRead$
-
- shuffleReadBytes() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- ShuffleReadMetricDistributions - org.apache.spark.status.api.v1中的类
-
- ShuffleReadMetrics - org.apache.spark.status.api.v1中的类
-
- shuffleReadMetrics() - 类 中的方法org.apache.spark.status.api.v1.TaskMetricDistributions
-
- shuffleReadMetrics() - 类 中的方法org.apache.spark.status.api.v1.TaskMetrics
-
- shuffleReadRecords() - 类 中的方法org.apache.spark.status.api.v1.ExecutorStageSummary
-
- shuffleReadRecords() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- shuffleRemoteBlocksFetched() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- shuffleRemoteBytesRead() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- shuffleRemoteBytesReadToDisk() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- ShuffleStatus - org.apache.spark中的类
-
Helper class used by the MapOutputTrackerMaster to perform bookkeeping for a single
ShuffleMapStage.
- ShuffleStatus(int) - 类 的构造器org.apache.spark.ShuffleStatus
-
- shuffleWrite() - 类 中的方法org.apache.spark.status.api.v1.ExecutorStageSummary
-
- shuffleWrite$() - 类 的构造器org.apache.spark.InternalAccumulator.shuffleWrite$
-
- shuffleWriteBytes() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- ShuffleWriteMetricDistributions - org.apache.spark.status.api.v1中的类
-
- ShuffleWriteMetrics - org.apache.spark.status.api.v1中的类
-
- shuffleWriteMetrics() - 类 中的方法org.apache.spark.status.api.v1.TaskMetricDistributions
-
- shuffleWriteMetrics() - 类 中的方法org.apache.spark.status.api.v1.TaskMetrics
-
- shuffleWriteRecords() - 类 中的方法org.apache.spark.status.api.v1.ExecutorStageSummary
-
- shuffleWriteRecords() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- shuffleWriterProcessor() - 类 中的方法org.apache.spark.ShuffleDependency
-
- shuffleWriteTime() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- shutdown() - 接口 中的方法org.apache.spark.ExecutorPlugin
-
Clean up and terminate this plugin.
- shutdown(ExecutorService, Duration) - 类 中的静态方法org.apache.spark.util.ThreadUtils
-
- Shutdown$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.Shutdown$
-
- ShutdownHookManager - org.apache.spark.util中的类
-
Various utility methods used by Spark.
- ShutdownHookManager() - 类 的构造器org.apache.spark.util.ShutdownHookManager
-
- sigma() - 类 中的方法org.apache.spark.mllib.stat.distribution.MultivariateGaussian
-
- sigmas() - 类 中的方法org.apache.spark.mllib.clustering.ExpectationSum
-
- SignalUtils - org.apache.spark.util中的类
-
Contains utilities for working with posix signals.
- SignalUtils() - 类 的构造器org.apache.spark.util.SignalUtils
-
- signum(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the signum of the given value.
- signum(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the signum of the given column.
- signum(T) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- signum(T) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- signum(T) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- signum(T) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- signum(T) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- signum(T) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- signum(T) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- SimpleFutureAction<T> - org.apache.spark中的类
-
A
FutureAction holding the result of an action that triggers a single job.
- simpleString() - 类 中的方法org.apache.spark.sql.types.ArrayType
-
- simpleString() - 类 中的静态方法org.apache.spark.sql.types.BinaryType
-
- simpleString() - 类 中的静态方法org.apache.spark.sql.types.BooleanType
-
- simpleString() - 类 中的方法org.apache.spark.sql.types.ByteType
-
- simpleString() - 类 中的方法org.apache.spark.sql.types.CalendarIntervalType
-
- simpleString() - 类 中的方法org.apache.spark.sql.types.CharType
-
- simpleString() - 类 中的方法org.apache.spark.sql.types.DataType
-
Readable string representation for the type.
- simpleString() - 类 中的静态方法org.apache.spark.sql.types.DateType
-
- simpleString() - 类 中的方法org.apache.spark.sql.types.DecimalType
-
- simpleString() - 类 中的静态方法org.apache.spark.sql.types.DoubleType
-
- simpleString() - 类 中的静态方法org.apache.spark.sql.types.FloatType
-
- simpleString() - 类 中的方法org.apache.spark.sql.types.IntegerType
-
- simpleString() - 类 中的方法org.apache.spark.sql.types.LongType
-
- simpleString() - 类 中的方法org.apache.spark.sql.types.MapType
-
- simpleString() - 类 中的静态方法org.apache.spark.sql.types.NullType
-
- simpleString() - 类 中的方法org.apache.spark.sql.types.ObjectType
-
- simpleString() - 类 中的方法org.apache.spark.sql.types.ShortType
-
- simpleString() - 类 中的静态方法org.apache.spark.sql.types.StringType
-
- simpleString() - 类 中的方法org.apache.spark.sql.types.StructType
-
- simpleString() - 类 中的静态方法org.apache.spark.sql.types.TimestampType
-
- simpleString() - 类 中的方法org.apache.spark.sql.types.VarcharType
-
- SimpleUpdater - org.apache.spark.mllib.optimization中的类
-
:: DeveloperApi ::
A simple updater for gradient descent *without* any regularization.
- SimpleUpdater() - 类 的构造器org.apache.spark.mllib.optimization.SimpleUpdater
-
- sin(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
- sin(String) - 类 中的静态方法org.apache.spark.sql.functions
-
- SingleSpillShuffleMapOutputWriter - org.apache.spark.shuffle.api中的接口
-
Optional extension for partition writing that is optimized for transferring a single
file to the backing store.
- SingleValueExecutorMetricType - org.apache.spark.metrics中的接口
-
- SingularValueDecomposition<UType,VType> - org.apache.spark.mllib.linalg中的类
-
Represents singular value decomposition (SVD) factors.
- SingularValueDecomposition(UType, Vector, VType) - 类 的构造器org.apache.spark.mllib.linalg.SingularValueDecomposition
-
- sinh(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
- sinh(String) - 类 中的静态方法org.apache.spark.sql.functions
-
- Sink - org.apache.spark.metrics.sink中的接口
-
- sink() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryProgress
-
- SinkProgress - org.apache.spark.sql.streaming中的类
-
Information about progress made for a sink in the execution of a
StreamingQuery
during a trigger.
- size() - 类 中的方法org.apache.spark.api.java.JavaUtils.SerializableMapWrapper
-
- size() - 类 中的方法org.apache.spark.ml.attribute.AttributeGroup
-
Size of the attribute group.
- size() - 类 中的方法org.apache.spark.ml.feature.VectorSizeHint
-
The size of Vectors in inputCol.
- size() - 类 中的方法org.apache.spark.ml.linalg.DenseVector
-
- size() - 类 中的方法org.apache.spark.ml.linalg.SparseVector
-
- size() - 接口 中的方法org.apache.spark.ml.linalg.Vector
-
Size of the vector.
- size() - 类 中的方法org.apache.spark.ml.param.ParamMap
-
Number of param pairs in this map.
- size() - 类 中的方法org.apache.spark.mllib.linalg.DenseVector
-
- size() - 类 中的方法org.apache.spark.mllib.linalg.SparseVector
-
- size() - 接口 中的方法org.apache.spark.mllib.linalg.Vector
-
Size of the vector.
- size(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns length of array or map.
- size() - 接口 中的方法org.apache.spark.sql.Row
-
Number of elements in the Row.
- size() - 类 中的方法org.apache.spark.sql.util.CaseInsensitiveStringMap
-
- size() - 接口 中的方法org.apache.spark.storage.BlockData
-
- size() - 类 中的方法org.apache.spark.storage.DiskBlockData
-
- size() - 类 中的方法org.apache.spark.storage.memory.DeserializedMemoryEntry
-
- size() - 接口 中的方法org.apache.spark.storage.memory.MemoryEntry
-
- size() - 类 中的方法org.apache.spark.storage.memory.SerializedMemoryEntry
-
- SIZE_IN_MEMORY() - 类 中的静态方法org.apache.spark.ui.storage.ToolTips
-
- SIZE_ON_DISK() - 类 中的静态方法org.apache.spark.ui.storage.ToolTips
-
- SizeEstimator - org.apache.spark.util中的类
-
:: DeveloperApi ::
Estimates the sizes of Java objects (number of bytes of memory they occupy), for use in
memory-aware caches.
- SizeEstimator() - 类 的构造器org.apache.spark.util.SizeEstimator
-
- sizeInBytes() - 接口 中的方法org.apache.spark.sql.connector.read.Statistics
-
- sizeInBytes() - 类 中的方法org.apache.spark.sql.sources.BaseRelation
-
Returns an estimated size of this relation in bytes.
- sketch(RDD<K>, int, ClassTag<K>) - 类 中的静态方法org.apache.spark.RangePartitioner
-
Sketches the input RDD via reservoir sampling on each partition.
- skewness(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the skewness of the values in a group.
- skewness(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the skewness of the values in a group.
- skip(long) - 类 中的方法org.apache.spark.io.NioBufferedFileInputStream
-
- skip(long) - 类 中的方法org.apache.spark.io.ReadAheadInputStream
-
- skip(long) - 类 中的方法org.apache.spark.storage.BufferReleasingInputStream
-
- skippedStages() - 类 中的方法org.apache.spark.status.LiveJob
-
- skippedTasks() - 类 中的方法org.apache.spark.status.LiveJob
-
- skipWhitespace() - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- slice(Column, int, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns an array containing all the elements in x from index start (or starting from the
end if start is negative) with the specified length.
- slice(Time, Time) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return all the RDDs between 'fromDuration' to 'toDuration' (both included)
- slice(org.apache.spark.streaming.Interval) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return all the RDDs defined by the Interval object (both end times included)
- slice(Time, Time) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return all the RDDs between 'fromTime' to 'toTime' (both included)
- slideDuration() - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Time interval after which the DStream generates an RDD
- slideDuration() - 类 中的方法org.apache.spark.streaming.dstream.InputDStream
-
- sliding(int, int) - 类 中的方法org.apache.spark.mllib.rdd.RDDFunctions
-
Returns an RDD from grouping items of its parent RDD in fixed size blocks by passing a sliding
window over them.
- sliding(int) - 类 中的方法org.apache.spark.mllib.rdd.RDDFunctions
-
sliding(Int, Int)* with step = 1.
- smoothing() - 类 中的方法org.apache.spark.ml.classification.NaiveBayes
-
- smoothing() - 类 中的方法org.apache.spark.ml.classification.NaiveBayesModel
-
- smoothing() - 接口 中的方法org.apache.spark.ml.classification.NaiveBayesParams
-
The smoothing parameter.
- SnappyCompressionCodec - org.apache.spark.io中的类
-
- SnappyCompressionCodec(SparkConf) - 类 的构造器org.apache.spark.io.SnappyCompressionCodec
-
- socketStream(String, int, Function<InputStream, Iterable<T>>, StorageLevel) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Create an input stream from network source hostname:port.
- socketStream(String, int, Function1<InputStream, Iterator<T>>, StorageLevel, ClassTag<T>) - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Creates an input stream from TCP source hostname:port.
- socketTextStream(String, int, StorageLevel) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Create an input stream from network source hostname:port.
- socketTextStream(String, int) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Create an input stream from network source hostname:port.
- socketTextStream(String, int, StorageLevel) - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Creates an input stream from TCP source hostname:port.
- solve(double, double, DenseVector, DenseVector, DenseVector) - 接口 中的方法org.apache.spark.ml.optim.NormalEquationSolver
-
Solve the normal equations from summary statistics.
- solve(ALS.NormalEquation, double) - 接口 中的方法org.apache.spark.ml.recommendation.ALS.LeastSquaresNESolver
-
Solves a least squares problem with regularization (possibly with other constraints).
- solve(double[], double[]) - 类 中的静态方法org.apache.spark.mllib.linalg.CholeskyDecomposition
-
Solves a symmetric positive definite linear system via Cholesky factorization.
- solve(double[], double[], NNLS.Workspace) - 类 中的静态方法org.apache.spark.mllib.optimization.NNLS
-
Solve a least squares problem, possibly with nonnegativity constraints, by a modified
projected gradient method.
- solver() - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
- solver() - 接口 中的方法org.apache.spark.ml.classification.MultilayerPerceptronParams
-
The solver algorithm for optimization.
- solver() - 接口 中的方法org.apache.spark.ml.param.shared.HasSolver
-
Param for the solver algorithm for optimization.
- solver() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
- solver() - 接口 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionBase
-
The solver algorithm for optimization.
- solver() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
- solver() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionTrainingSummary
-
- solver() - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
- solver() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionModel
-
- solver() - 接口 中的方法org.apache.spark.ml.regression.LinearRegressionParams
-
The solver algorithm for optimization.
- Sort() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.QuantileStrategy
-
- sort(String, String...) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset sorted by the specified column, all in ascending order.
- sort(Column...) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset sorted by the given expressions.
- sort(String, Seq<String>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset sorted by the specified column, all in ascending order.
- sort(Seq<Column>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset sorted by the given expressions.
- sort_array(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Sorts the input array for the given column in ascending order,
according to the natural ordering of the array elements.
- sort_array(Column, boolean) - 类 中的静态方法org.apache.spark.sql.functions
-
Sorts the input array for the given column in ascending or descending order,
according to the natural ordering of the array elements.
- sortBy(Function<T, S>, boolean, int) - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Return this RDD sorted by the given key function.
- sortBy(Function1<T, K>, boolean, int, Ordering<K>, ClassTag<K>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return this RDD sorted by the given key function.
- sortBy(String, String...) - 类 中的方法org.apache.spark.sql.DataFrameWriter
-
Sorts the output in each bucket by the given columns.
- sortBy(String, Seq<String>) - 类 中的方法org.apache.spark.sql.DataFrameWriter
-
Sorts the output in each bucket by the given columns.
- sortByKey() - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Sort the RDD by key, so that each partition contains a sorted range of the elements in
ascending order.
- sortByKey(boolean) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Sort the RDD by key, so that each partition contains a sorted range of the elements.
- sortByKey(boolean, int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Sort the RDD by key, so that each partition contains a sorted range of the elements.
- sortByKey(Comparator<K>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Sort the RDD by key, so that each partition contains a sorted range of the elements.
- sortByKey(Comparator<K>, boolean) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Sort the RDD by key, so that each partition contains a sorted range of the elements.
- sortByKey(Comparator<K>, boolean, int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Sort the RDD by key, so that each partition contains a sorted range of the elements.
- sortByKey(boolean, int) - 类 中的方法org.apache.spark.rdd.OrderedRDDFunctions
-
Sort the RDD by key, so that each partition contains a sorted range of the elements.
- sortWithinPartitions(String, String...) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset with each partition sorted by the given expressions.
- sortWithinPartitions(Column...) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset with each partition sorted by the given expressions.
- sortWithinPartitions(String, Seq<String>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset with each partition sorted by the given expressions.
- sortWithinPartitions(Seq<Column>) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns a new Dataset with each partition sorted by the given expressions.
- soundex(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the soundex code for the specified expression.
- Source - org.apache.spark.metrics.source中的接口
-
- sourceName() - 类 中的静态方法org.apache.spark.metrics.source.CodegenMetrics
-
- sourceName() - 类 中的静态方法org.apache.spark.metrics.source.HiveCatalogMetrics
-
- sourceName() - 接口 中的方法org.apache.spark.metrics.source.Source
-
- SourceProgress - org.apache.spark.sql.streaming中的类
-
Information about progress made for a source in the execution of a
StreamingQuery
during a trigger.
- sources() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryProgress
-
- sourceSchema(SQLContext, Option<StructType>, String, Map<String, String>) - 接口 中的方法org.apache.spark.sql.sources.StreamSourceProvider
-
Returns the name and schema of the source that can be used to continually read data.
- spark() - 类 中的方法org.apache.spark.status.api.v1.VersionInfo
-
- SPARK_CONNECTOR_NAME() - 类 中的静态方法org.apache.spark.ui.JettyUtils
-
- SPARK_CONTEXT_SHUTDOWN_PRIORITY() - 类 中的静态方法org.apache.spark.util.ShutdownHookManager
-
The shutdown priority of the SparkContext instance.
- SPARK_IO_ENCRYPTION_COMMONS_CONFIG_PREFIX() - 类 中的静态方法org.apache.spark.security.CryptoStreamUtils
-
- SPARK_MASTER - 类 中的静态变量org.apache.spark.launcher.SparkLauncher
-
The Spark master.
- spark_partition_id() - 类 中的静态方法org.apache.spark.sql.functions
-
Partition ID.
- SPARK_REGEX() - 类 中的静态方法org.apache.spark.SparkMasterRegex
-
- SPARK_WORKER_PREFIX() - 类 中的静态方法org.apache.spark.internal.config.Worker
-
- SPARK_WORKER_RESOURCE_FILE() - 类 中的静态方法org.apache.spark.internal.config.Worker
-
- SparkAppConfig(Seq<Tuple2<String, String>>, Option<byte[]>, Option<byte[]>) - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.SparkAppConfig
-
- SparkAppConfig$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.SparkAppConfig$
-
- SparkAppHandle - org.apache.spark.launcher中的接口
-
A handle to a running Spark application.
- SparkAppHandle.Listener - org.apache.spark.launcher中的接口
-
Listener for updates to a handle's state.
- SparkAppHandle.State - org.apache.spark.launcher中的枚举
-
Represents the application's state.
- SparkAWSCredentials - org.apache.spark.streaming.kinesis中的接口
-
Serializable interface providing a method executors can call to obtain an
AWSCredentialsProvider instance for authenticating to AWS services.
- SparkAWSCredentials.Builder - org.apache.spark.streaming.kinesis中的类
-
- sparkConf - 类 中的变量org.apache.spark.ExecutorPluginContext
-
- SparkConf - org.apache.spark中的类
-
Configuration for a Spark application.
- SparkConf(boolean) - 类 的构造器org.apache.spark.SparkConf
-
- SparkConf() - 类 的构造器org.apache.spark.SparkConf
-
Create a SparkConf that loads defaults from system properties and the classpath
- sparkContext() - 类 中的方法org.apache.spark.rdd.RDD
-
The SparkContext that created this RDD.
- SparkContext - org.apache.spark中的类
-
Main entry point for Spark functionality.
- SparkContext(SparkConf) - 类 的构造器org.apache.spark.SparkContext
-
- SparkContext() - 类 的构造器org.apache.spark.SparkContext
-
Create a SparkContext that loads settings from system properties (for instance, when
launching with .
- SparkContext(String, String, SparkConf) - 类 的构造器org.apache.spark.SparkContext
-
Alternative constructor that allows setting common Spark properties directly
- SparkContext(String, String, String, Seq<String>, Map<String, String>) - 类 的构造器org.apache.spark.SparkContext
-
Alternative constructor that allows setting common Spark properties directly
- sparkContext() - 类 中的方法org.apache.spark.sql.SparkSession
-
- sparkContext() - 类 中的方法org.apache.spark.sql.SQLContext
-
- sparkContext() - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
The underlying SparkContext
- sparkContext() - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Return the associated Spark context
- SparkDataStream - org.apache.spark.sql.connector.read.streaming中的接口
-
The base interface representing a readable data stream in a Spark streaming query.
- SparkEnv - org.apache.spark中的类
-
:: DeveloperApi ::
Holds all the runtime environment objects for a running Spark instance (either master or worker),
including the serializer, RpcEnv, block manager, map output tracker, etc.
- SparkEnv(String, org.apache.spark.rpc.RpcEnv, Serializer, Serializer, org.apache.spark.serializer.SerializerManager, MapOutputTracker, ShuffleManager, org.apache.spark.broadcast.BroadcastManager, org.apache.spark.storage.BlockManager, SecurityManager, org.apache.spark.metrics.MetricsSystem, MemoryManager, org.apache.spark.scheduler.OutputCommitCoordinator, SparkConf) - 类 的构造器org.apache.spark.SparkEnv
-
- sparkEventFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- sparkEventToJson(SparkListenerEvent) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
------------------------------------------------- *
JSON serialization methods for SparkListenerEvents |
- SparkException - org.apache.spark中的异常错误
-
- SparkException(String, Throwable) - 异常错误 的构造器org.apache.spark.SparkException
-
- SparkException(String) - 异常错误 的构造器org.apache.spark.SparkException
-
- SparkExecutorInfo - org.apache.spark中的接口
-
Exposes information about Spark Executors.
- SparkExecutorInfoImpl - org.apache.spark中的类
-
- SparkExecutorInfoImpl(String, int, long, int, long, long, long, long) - 类 的构造器org.apache.spark.SparkExecutorInfoImpl
-
- SparkExitCode - org.apache.spark.util中的类
-
- SparkExitCode() - 类 的构造器org.apache.spark.util.SparkExitCode
-
- SparkFiles - org.apache.spark中的类
-
Resolves paths to files added through SparkContext.addFile().
- SparkFiles() - 类 的构造器org.apache.spark.SparkFiles
-
- SparkFirehoseListener - org.apache.spark中的类
-
Class that allows users to receive all SparkListener events.
- SparkFirehoseListener() - 类 的构造器org.apache.spark.SparkFirehoseListener
-
- SparkHadoopMapRedUtil - org.apache.spark.mapred中的类
-
- SparkHadoopMapRedUtil() - 类 的构造器org.apache.spark.mapred.SparkHadoopMapRedUtil
-
- SparkHadoopWriter - org.apache.spark.internal.io中的类
-
A helper object that saves an RDD using a Hadoop OutputFormat.
- SparkHadoopWriter() - 类 的构造器org.apache.spark.internal.io.SparkHadoopWriter
-
- SparkHadoopWriterUtils - org.apache.spark.internal.io中的类
-
A helper object that provide common utils used during saving an RDD using a Hadoop OutputFormat
(both from the old mapred API and the new mapreduce API)
- SparkHadoopWriterUtils() - 类 的构造器org.apache.spark.internal.io.SparkHadoopWriterUtils
-
- sparkJavaOpts(SparkConf, Function1<String, Object>) - 类 中的静态方法org.apache.spark.util.Utils
-
Convert all spark properties set in the given SparkConf to a sequence of java options.
- SparkJobInfo - org.apache.spark中的接口
-
Exposes information about Spark Jobs.
- SparkJobInfoImpl - org.apache.spark中的类
-
- SparkJobInfoImpl(int, int[], JobExecutionStatus) - 类 的构造器org.apache.spark.SparkJobInfoImpl
-
- SparkLauncher - org.apache.spark.launcher中的类
-
Launcher for Spark applications.
- SparkLauncher() - 类 的构造器org.apache.spark.launcher.SparkLauncher
-
- SparkLauncher(Map<String, String>) - 类 的构造器org.apache.spark.launcher.SparkLauncher
-
Creates a launcher that will set the given environment variables in the child.
- SparkListener - org.apache.spark.scheduler中的类
-
:: DeveloperApi ::
A default implementation for SparkListenerInterface that has no-op implementations for
all callbacks.
- SparkListener() - 类 的构造器org.apache.spark.scheduler.SparkListener
-
- SparkListenerApplicationEnd - org.apache.spark.scheduler中的类
-
- SparkListenerApplicationEnd(long) - 类 的构造器org.apache.spark.scheduler.SparkListenerApplicationEnd
-
- SparkListenerApplicationStart - org.apache.spark.scheduler中的类
-
- SparkListenerApplicationStart(String, Option<String>, long, String, Option<String>, Option<Map<String, String>>, Option<Map<String, String>>) - 类 的构造器org.apache.spark.scheduler.SparkListenerApplicationStart
-
- SparkListenerBlockManagerAdded - org.apache.spark.scheduler中的类
-
- SparkListenerBlockManagerAdded(long, BlockManagerId, long, Option<Object>, Option<Object>) - 类 的构造器org.apache.spark.scheduler.SparkListenerBlockManagerAdded
-
- SparkListenerBlockManagerRemoved - org.apache.spark.scheduler中的类
-
- SparkListenerBlockManagerRemoved(long, BlockManagerId) - 类 的构造器org.apache.spark.scheduler.SparkListenerBlockManagerRemoved
-
- SparkListenerBlockUpdated - org.apache.spark.scheduler中的类
-
- SparkListenerBlockUpdated(BlockUpdatedInfo) - 类 的构造器org.apache.spark.scheduler.SparkListenerBlockUpdated
-
- SparkListenerBus - org.apache.spark.scheduler中的接口
-
- SparkListenerEnvironmentUpdate - org.apache.spark.scheduler中的类
-
- SparkListenerEnvironmentUpdate(Map<String, Seq<Tuple2<String, String>>>) - 类 的构造器org.apache.spark.scheduler.SparkListenerEnvironmentUpdate
-
- SparkListenerEvent - org.apache.spark.scheduler中的接口
-
- SparkListenerExecutorAdded - org.apache.spark.scheduler中的类
-
- SparkListenerExecutorAdded(long, String, ExecutorInfo) - 类 的构造器org.apache.spark.scheduler.SparkListenerExecutorAdded
-
- SparkListenerExecutorBlacklisted - org.apache.spark.scheduler中的类
-
- SparkListenerExecutorBlacklisted(long, String, int) - 类 的构造器org.apache.spark.scheduler.SparkListenerExecutorBlacklisted
-
- SparkListenerExecutorBlacklistedForStage - org.apache.spark.scheduler中的类
-
- SparkListenerExecutorBlacklistedForStage(long, String, int, int, int) - 类 的构造器org.apache.spark.scheduler.SparkListenerExecutorBlacklistedForStage
-
- SparkListenerExecutorMetricsUpdate - org.apache.spark.scheduler中的类
-
Periodic updates from executors.
- SparkListenerExecutorMetricsUpdate(String, Seq<Tuple4<Object, Object, Object, Seq<AccumulableInfo>>>, Map<Tuple2<Object, Object>, ExecutorMetrics>) - 类 的构造器org.apache.spark.scheduler.SparkListenerExecutorMetricsUpdate
-
- SparkListenerExecutorRemoved - org.apache.spark.scheduler中的类
-
- SparkListenerExecutorRemoved(long, String, String) - 类 的构造器org.apache.spark.scheduler.SparkListenerExecutorRemoved
-
- SparkListenerExecutorUnblacklisted - org.apache.spark.scheduler中的类
-
- SparkListenerExecutorUnblacklisted(long, String) - 类 的构造器org.apache.spark.scheduler.SparkListenerExecutorUnblacklisted
-
- SparkListenerInterface - org.apache.spark.scheduler中的接口
-
Interface for listening to events from the Spark scheduler.
- SparkListenerJobEnd - org.apache.spark.scheduler中的类
-
- SparkListenerJobEnd(int, long, JobResult) - 类 的构造器org.apache.spark.scheduler.SparkListenerJobEnd
-
- SparkListenerJobStart - org.apache.spark.scheduler中的类
-
- SparkListenerJobStart(int, long, Seq<StageInfo>, Properties) - 类 的构造器org.apache.spark.scheduler.SparkListenerJobStart
-
- SparkListenerLogStart - org.apache.spark.scheduler中的类
-
An internal class that describes the metadata of an event log.
- SparkListenerLogStart(String) - 类 的构造器org.apache.spark.scheduler.SparkListenerLogStart
-
- SparkListenerNodeBlacklisted - org.apache.spark.scheduler中的类
-
- SparkListenerNodeBlacklisted(long, String, int) - 类 的构造器org.apache.spark.scheduler.SparkListenerNodeBlacklisted
-
- SparkListenerNodeBlacklistedForStage - org.apache.spark.scheduler中的类
-
- SparkListenerNodeBlacklistedForStage(long, String, int, int, int) - 类 的构造器org.apache.spark.scheduler.SparkListenerNodeBlacklistedForStage
-
- SparkListenerNodeUnblacklisted - org.apache.spark.scheduler中的类
-
- SparkListenerNodeUnblacklisted(long, String) - 类 的构造器org.apache.spark.scheduler.SparkListenerNodeUnblacklisted
-
- SparkListenerSpeculativeTaskSubmitted - org.apache.spark.scheduler中的类
-
- SparkListenerSpeculativeTaskSubmitted(int, int) - 类 的构造器org.apache.spark.scheduler.SparkListenerSpeculativeTaskSubmitted
-
- SparkListenerStageCompleted - org.apache.spark.scheduler中的类
-
- SparkListenerStageCompleted(StageInfo) - 类 的构造器org.apache.spark.scheduler.SparkListenerStageCompleted
-
- SparkListenerStageExecutorMetrics - org.apache.spark.scheduler中的类
-
Peak metric values for the executor for the stage, written to the history log at stage
completion.
- SparkListenerStageExecutorMetrics(String, int, int, ExecutorMetrics) - 类 的构造器org.apache.spark.scheduler.SparkListenerStageExecutorMetrics
-
- SparkListenerStageSubmitted - org.apache.spark.scheduler中的类
-
- SparkListenerStageSubmitted(StageInfo, Properties) - 类 的构造器org.apache.spark.scheduler.SparkListenerStageSubmitted
-
- SparkListenerTaskEnd - org.apache.spark.scheduler中的类
-
- SparkListenerTaskEnd(int, int, String, TaskEndReason, TaskInfo, ExecutorMetrics, TaskMetrics) - 类 的构造器org.apache.spark.scheduler.SparkListenerTaskEnd
-
- SparkListenerTaskGettingResult - org.apache.spark.scheduler中的类
-
- SparkListenerTaskGettingResult(TaskInfo) - 类 的构造器org.apache.spark.scheduler.SparkListenerTaskGettingResult
-
- SparkListenerTaskStart - org.apache.spark.scheduler中的类
-
- SparkListenerTaskStart(int, int, TaskInfo) - 类 的构造器org.apache.spark.scheduler.SparkListenerTaskStart
-
- SparkListenerUnpersistRDD - org.apache.spark.scheduler中的类
-
- SparkListenerUnpersistRDD(int) - 类 的构造器org.apache.spark.scheduler.SparkListenerUnpersistRDD
-
- SparkMasterRegex - org.apache.spark中的类
-
A collection of regexes for extracting information from the master string.
- SparkMasterRegex() - 类 的构造器org.apache.spark.SparkMasterRegex
-
- sparkProperties() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.SparkAppConfig
-
- sparkProperties() - 类 中的方法org.apache.spark.status.api.v1.ApplicationEnvironmentInfo
-
- SPARKR_COMMAND() - 类 中的静态方法org.apache.spark.internal.config.R
-
- sparkRPackagePath(boolean) - 类 中的静态方法org.apache.spark.api.r.RUtils
-
Get the list of paths for R packages in various deployment modes, of which the first
path is for the SparkR package itself.
- sparkSession() - 接口 中的方法org.apache.spark.ml.util.BaseReadWrite
-
Returns the user-specified Spark Session or the default.
- sparkSession() - 类 中的方法org.apache.spark.sql.Dataset
-
- sparkSession() - 类 中的方法org.apache.spark.sql.dynamicpruning.PlanDynamicPruningFilters
-
- sparkSession() - 接口 中的方法org.apache.spark.sql.hive.HiveStrategies
-
- SparkSession - org.apache.spark.sql中的类
-
The entry point to programming Spark with the Dataset and DataFrame API.
- sparkSession() - 类 中的方法org.apache.spark.sql.SQLContext
-
- sparkSession() - 接口 中的方法org.apache.spark.sql.streaming.StreamingQuery
-
Returns the SparkSession associated with this.
- SparkSession.Builder - org.apache.spark.sql中的类
-
- SparkSession.implicits$ - org.apache.spark.sql中的类
-
(Scala-specific) Implicit methods available in Scala for converting
common Scala objects into DataFrames.
- SparkSessionExtensions - org.apache.spark.sql中的类
-
:: Experimental ::
Holder for injection points to the
SparkSession.
- SparkSessionExtensions() - 类 的构造器org.apache.spark.sql.SparkSessionExtensions
-
- SparkShellLoggingFilter - org.apache.spark.internal中的类
-
- SparkShellLoggingFilter() - 类 的构造器org.apache.spark.internal.SparkShellLoggingFilter
-
- SparkShutdownHook - org.apache.spark.util中的类
-
- SparkShutdownHook(int, Function0<BoxedUnit>) - 类 的构造器org.apache.spark.util.SparkShutdownHook
-
- SparkStageInfo - org.apache.spark中的接口
-
Exposes information about Spark Stages.
- SparkStageInfoImpl - org.apache.spark中的类
-
- SparkStageInfoImpl(int, int, long, String, int, int, int, int) - 类 的构造器org.apache.spark.SparkStageInfoImpl
-
- SparkStatusTracker - org.apache.spark中的类
-
Low-level status reporting APIs for monitoring job and stage progress.
- sparkUser() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
- sparkUser() - 类 中的方法org.apache.spark.scheduler.SparkListenerApplicationStart
-
- sparkUser() - 类 中的方法org.apache.spark.SparkContext
-
- sparkUser() - 类 中的方法org.apache.spark.status.api.v1.ApplicationAttemptInfo
-
- sparkVersion() - 类 中的方法org.apache.spark.scheduler.SparkListenerLogStart
-
- sparse(int, int, int[], int[], double[]) - 类 中的静态方法org.apache.spark.ml.linalg.Matrices
-
Creates a column-major sparse matrix in Compressed Sparse Column (CSC) format.
- sparse(int, int[], double[]) - 类 中的静态方法org.apache.spark.ml.linalg.Vectors
-
Creates a sparse vector providing its index array and value array.
- sparse(int, Seq<Tuple2<Object, Object>>) - 类 中的静态方法org.apache.spark.ml.linalg.Vectors
-
Creates a sparse vector using unordered (index, value) pairs.
- sparse(int, Iterable<Tuple2<Integer, Double>>) - 类 中的静态方法org.apache.spark.ml.linalg.Vectors
-
Creates a sparse vector using unordered (index, value) pairs in a Java friendly way.
- sparse(int, int, int[], int[], double[]) - 类 中的静态方法org.apache.spark.mllib.linalg.Matrices
-
Creates a column-major sparse matrix in Compressed Sparse Column (CSC) format.
- sparse(int, int[], double[]) - 类 中的静态方法org.apache.spark.mllib.linalg.Vectors
-
Creates a sparse vector providing its index array and value array.
- sparse(int, Seq<Tuple2<Object, Object>>) - 类 中的静态方法org.apache.spark.mllib.linalg.Vectors
-
Creates a sparse vector using unordered (index, value) pairs.
- sparse(int, Iterable<Tuple2<Integer, Double>>) - 类 中的静态方法org.apache.spark.mllib.linalg.Vectors
-
Creates a sparse vector using unordered (index, value) pairs in a Java friendly way.
- SparseMatrix - org.apache.spark.ml.linalg中的类
-
Column-major sparse matrix.
- SparseMatrix(int, int, int[], int[], double[], boolean) - 类 的构造器org.apache.spark.ml.linalg.SparseMatrix
-
- SparseMatrix(int, int, int[], int[], double[]) - 类 的构造器org.apache.spark.ml.linalg.SparseMatrix
-
Column-major sparse matrix.
- SparseMatrix - org.apache.spark.mllib.linalg中的类
-
Column-major sparse matrix.
- SparseMatrix(int, int, int[], int[], double[], boolean) - 类 的构造器org.apache.spark.mllib.linalg.SparseMatrix
-
- SparseMatrix(int, int, int[], int[], double[]) - 类 的构造器org.apache.spark.mllib.linalg.SparseMatrix
-
Column-major sparse matrix.
- SparseVector - org.apache.spark.ml.linalg中的类
-
A sparse vector represented by an index array and a value array.
- SparseVector(int, int[], double[]) - 类 的构造器org.apache.spark.ml.linalg.SparseVector
-
- SparseVector - org.apache.spark.mllib.linalg中的类
-
A sparse vector represented by an index array and a value array.
- SparseVector(int, int[], double[]) - 类 的构造器org.apache.spark.mllib.linalg.SparseVector
-
- SPARSITY() - 类 中的静态方法org.apache.spark.ml.attribute.AttributeKeys
-
- sparsity() - 类 中的方法org.apache.spark.ml.attribute.NumericAttribute
-
- spdiag(Vector) - 类 中的静态方法org.apache.spark.ml.linalg.SparseMatrix
-
Generate a diagonal matrix in SparseMatrix format from the supplied values.
- spdiag(Vector) - 类 中的静态方法org.apache.spark.mllib.linalg.SparseMatrix
-
Generate a diagonal matrix in SparseMatrix format from the supplied values.
- SpearmanCorrelation - org.apache.spark.mllib.stat.correlation中的类
-
Compute Spearman's correlation for two RDDs of the type RDD[Double] or the correlation matrix
for an RDD of the type RDD[Vector].
- SpearmanCorrelation() - 类 的构造器org.apache.spark.mllib.stat.correlation.SpearmanCorrelation
-
- SpecialLengths - org.apache.spark.api.r中的类
-
- SpecialLengths() - 类 的构造器org.apache.spark.api.r.SpecialLengths
-
- speculative() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
- speculative() - 类 中的方法org.apache.spark.status.api.v1.TaskData
-
- speye(int) - 类 中的静态方法org.apache.spark.ml.linalg.Matrices
-
Generate a sparse Identity Matrix in Matrix format.
- speye(int) - 类 中的静态方法org.apache.spark.ml.linalg.SparseMatrix
-
Generate an Identity Matrix in SparseMatrix format.
- speye(int) - 类 中的静态方法org.apache.spark.mllib.linalg.Matrices
-
Generate a sparse Identity Matrix in Matrix format.
- speye(int) - 类 中的静态方法org.apache.spark.mllib.linalg.SparseMatrix
-
Generate an Identity Matrix in SparseMatrix format.
- SpillListener - org.apache.spark中的类
-
A SparkListener that detects whether spills have occurred in Spark jobs.
- SpillListener() - 类 的构造器org.apache.spark.SpillListener
-
- split() - 类 中的方法org.apache.spark.ml.tree.DecisionTreeModelReadWrite.NodeData
-
- split() - 类 中的方法org.apache.spark.ml.tree.InternalNode
-
- Split - org.apache.spark.ml.tree中的接口
-
Interface for a "Split," which specifies a test made at a decision tree node
to choose the left or right path.
- split() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.NodeData
-
- split() - 类 中的方法org.apache.spark.mllib.tree.model.Node
-
- Split - org.apache.spark.mllib.tree.model中的类
-
:: DeveloperApi ::
Split applied to a feature
param: feature feature index
param: threshold Threshold for continuous feature.
- Split(int, double, Enumeration.Value, List<Object>) - 类 的构造器org.apache.spark.mllib.tree.model.Split
-
- split(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Splits str around matches of the given regex.
- split(Column, String, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Splits str around matches of the given regex.
- splitAndCountPartitions(Iterator<String>) - 类 中的静态方法org.apache.spark.streaming.util.RawTextHelper
-
Splits lines and counts the words.
- splitCommandString(String) - 类 中的静态方法org.apache.spark.util.Utils
-
Split a string of potentially quoted arguments from the command line the way that a shell
would do it to determine arguments to a command.
- SplitData(int, double[], int) - 类 的构造器org.apache.spark.ml.tree.DecisionTreeModelReadWrite.SplitData
-
- SplitData(int, double, int, Seq<Object>) - 类 的构造器org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.SplitData
-
- SplitData$() - 类 的构造器org.apache.spark.ml.tree.DecisionTreeModelReadWrite.SplitData$
-
- SplitData$() - 类 的构造器org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.SplitData$
-
- splitIndex() - 类 中的方法org.apache.spark.storage.RDDBlockId
-
- SplitInfo - org.apache.spark.scheduler中的类
-
- SplitInfo(Class<?>, String, String, long, Object) - 类 的构造器org.apache.spark.scheduler.SplitInfo
-
- splits() - 类 中的方法org.apache.spark.ml.feature.Bucketizer
-
Parameter for mapping continuous features into buckets.
- splitsArray() - 类 中的方法org.apache.spark.ml.feature.Bucketizer
-
Parameter for specifying multiple splits parameters.
- spr(double, Vector, DenseVector) - 类 中的静态方法org.apache.spark.ml.linalg.BLAS
-
Adds alpha * x * x.t to a matrix in-place.
- spr(double, Vector, double[]) - 类 中的静态方法org.apache.spark.ml.linalg.BLAS
-
Adds alpha * x * x.t to a matrix in-place.
- spr(double, Vector, DenseVector) - 类 中的静态方法org.apache.spark.mllib.linalg.BLAS
-
Adds alpha * v * v.t to a matrix in-place.
- spr(double, Vector, double[]) - 类 中的静态方法org.apache.spark.mllib.linalg.BLAS
-
Adds alpha * v * v.t to a matrix in-place.
- sprand(int, int, double, Random) - 类 中的静态方法org.apache.spark.ml.linalg.Matrices
-
Generate a SparseMatrix consisting of i.i.d.
- sprand(int, int, double, Random) - 类 中的静态方法org.apache.spark.ml.linalg.SparseMatrix
-
Generate a SparseMatrix consisting of i.i.d. uniform random numbers.
- sprand(int, int, double, Random) - 类 中的静态方法org.apache.spark.mllib.linalg.Matrices
-
Generate a SparseMatrix consisting of i.i.d.
- sprand(int, int, double, Random) - 类 中的静态方法org.apache.spark.mllib.linalg.SparseMatrix
-
Generate a SparseMatrix consisting of i.i.d. uniform random numbers.
- sprandn(int, int, double, Random) - 类 中的静态方法org.apache.spark.ml.linalg.Matrices
-
Generate a SparseMatrix consisting of i.i.d.
- sprandn(int, int, double, Random) - 类 中的静态方法org.apache.spark.ml.linalg.SparseMatrix
-
Generate a SparseMatrix consisting of i.i.d. gaussian random numbers.
- sprandn(int, int, double, Random) - 类 中的静态方法org.apache.spark.mllib.linalg.Matrices
-
Generate a SparseMatrix consisting of i.i.d.
- sprandn(int, int, double, Random) - 类 中的静态方法org.apache.spark.mllib.linalg.SparseMatrix
-
Generate a SparseMatrix consisting of i.i.d. gaussian random numbers.
- SPREAD_OUT_APPS() - 类 中的静态方法org.apache.spark.internal.config.Deploy
-
- sqdist(Vector, Vector) - 类 中的静态方法org.apache.spark.ml.linalg.Vectors
-
Returns the squared distance between two Vectors.
- sqdist(Vector, Vector) - 类 中的静态方法org.apache.spark.mllib.linalg.Vectors
-
Returns the squared distance between two Vectors.
- sql(String) - 类 中的方法org.apache.spark.sql.SparkSession
-
Executes a SQL query using Spark, returning the result as a DataFrame.
- sql(String) - 类 中的方法org.apache.spark.sql.SQLContext
-
Executes a SQL query using Spark, returning the result as a DataFrame.
- sql() - 类 中的方法org.apache.spark.sql.types.ArrayType
-
- sql() - 类 中的静态方法org.apache.spark.sql.types.BinaryType
-
- sql() - 类 中的静态方法org.apache.spark.sql.types.BooleanType
-
- sql() - 类 中的静态方法org.apache.spark.sql.types.ByteType
-
- sql() - 类 中的静态方法org.apache.spark.sql.types.CalendarIntervalType
-
- sql() - 类 中的方法org.apache.spark.sql.types.DataType
-
- sql() - 类 中的静态方法org.apache.spark.sql.types.DateType
-
- sql() - 类 中的方法org.apache.spark.sql.types.DecimalType
-
- sql() - 类 中的静态方法org.apache.spark.sql.types.DoubleType
-
- sql() - 类 中的静态方法org.apache.spark.sql.types.FloatType
-
- sql() - 类 中的静态方法org.apache.spark.sql.types.IntegerType
-
- sql() - 类 中的静态方法org.apache.spark.sql.types.LongType
-
- sql() - 类 中的方法org.apache.spark.sql.types.MapType
-
- sql() - 类 中的静态方法org.apache.spark.sql.types.NullType
-
- sql() - 类 中的静态方法org.apache.spark.sql.types.ShortType
-
- sql() - 类 中的静态方法org.apache.spark.sql.types.StringType
-
- sql() - 类 中的方法org.apache.spark.sql.types.StructType
-
- sql() - 类 中的静态方法org.apache.spark.sql.types.TimestampType
-
- sqlContext() - 接口 中的方法org.apache.spark.ml.util.BaseReadWrite
-
Returns the user-specified SQL context or the default.
- sqlContext() - 类 中的方法org.apache.spark.sql.Dataset
-
- sqlContext() - 类 中的方法org.apache.spark.sql.sources.BaseRelation
-
- sqlContext() - 类 中的方法org.apache.spark.sql.SparkSession
-
A wrapped version of this session in the form of a
SQLContext, for backward compatibility.
- SQLContext - org.apache.spark.sql中的类
-
The entry point for working with structured data (rows and columns) in Spark 1.x.
- SQLContext.implicits$ - org.apache.spark.sql中的类
-
(Scala-specific) Implicit methods available in Scala for converting
common Scala objects into DataFrames.
- SQLDataTypes - org.apache.spark.ml.linalg中的类
-
:: DeveloperApi ::
SQL data types for vectors and matrices.
- SQLDataTypes() - 类 的构造器org.apache.spark.ml.linalg.SQLDataTypes
-
- SQLImplicits - org.apache.spark.sql中的类
-
A collection of implicit methods for converting common Scala objects into
Datasets.
- SQLImplicits() - 类 的构造器org.apache.spark.sql.SQLImplicits
-
- SQLImplicits.StringToColumn - org.apache.spark.sql中的类
-
Converts $"col name" into a
Column.
- SQLTransformer - org.apache.spark.ml.feature中的类
-
Implements the transformations which are defined by SQL statement.
- SQLTransformer(String) - 类 的构造器org.apache.spark.ml.feature.SQLTransformer
-
- SQLTransformer() - 类 的构造器org.apache.spark.ml.feature.SQLTransformer
-
- sqlType() - 类 中的方法org.apache.spark.mllib.linalg.VectorUDT
-
- SQLUserDefinedType - org.apache.spark.sql.types中的注释类型
-
::DeveloperApi::
A user-defined type which can be automatically recognized by a SQLContext and registered.
- SQLUtils - org.apache.spark.sql.api.r中的类
-
- SQLUtils() - 类 的构造器org.apache.spark.sql.api.r.SQLUtils
-
- sqrt(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the square root of the specified float value.
- sqrt(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Computes the square root of the specified float value.
- Sqrt$() - 类 的构造器org.apache.spark.ml.regression.GeneralizedLinearRegression.Sqrt$
-
- SquaredError - org.apache.spark.mllib.tree.loss中的类
-
:: DeveloperApi ::
Class for squared error loss calculation.
- SquaredError() - 类 的构造器org.apache.spark.mllib.tree.loss.SquaredError
-
- SquaredEuclideanSilhouette - org.apache.spark.ml.evaluation中的类
-
SquaredEuclideanSilhouette computes the average of the
Silhouette over all the data of the dataset, which is
a measure of how appropriately the data have been clustered.
- SquaredEuclideanSilhouette() - 类 的构造器org.apache.spark.ml.evaluation.SquaredEuclideanSilhouette
-
- SquaredEuclideanSilhouette.ClusterStats - org.apache.spark.ml.evaluation中的类
-
- SquaredEuclideanSilhouette.ClusterStats$ - org.apache.spark.ml.evaluation中的类
-
- SquaredL2Updater - org.apache.spark.mllib.optimization中的类
-
:: DeveloperApi ::
Updater for L2 regularized problems.
- SquaredL2Updater() - 类 的构造器org.apache.spark.mllib.optimization.SquaredL2Updater
-
- squaredNormSum() - 类 中的方法org.apache.spark.ml.evaluation.SquaredEuclideanSilhouette.ClusterStats
-
- Src - 类 中的静态变量org.apache.spark.graphx.TripletFields
-
Expose the source and edge fields but not the destination field.
- srcAttr() - 类 中的方法org.apache.spark.graphx.EdgeContext
-
The vertex attribute of the edge's source vertex.
- srcAttr() - 类 中的方法org.apache.spark.graphx.EdgeTriplet
-
The source vertex attribute
- srcAttr() - 类 中的方法org.apache.spark.graphx.impl.AggregatingEdgeContext
-
- srcCol() - 类 中的方法org.apache.spark.ml.clustering.PowerIterationClustering
-
- srcCol() - 接口 中的方法org.apache.spark.ml.clustering.PowerIterationClusteringParams
-
Param for the name of the input column for source vertex IDs.
- srcId() - 类 中的方法org.apache.spark.graphx.Edge
-
- srcId() - 类 中的方法org.apache.spark.graphx.EdgeContext
-
The vertex id of the edge's source vertex.
- srcId() - 类 中的方法org.apache.spark.graphx.impl.AggregatingEdgeContext
-
- srdd() - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
- ssc() - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
- stackTrace() - 类 中的方法org.apache.spark.ExceptionFailure
-
- StackTrace - org.apache.spark.status.api.v1中的类
-
- StackTrace(Seq<String>) - 类 的构造器org.apache.spark.status.api.v1.StackTrace
-
- stackTrace() - 类 中的方法org.apache.spark.status.api.v1.ThreadStackTrace
-
- stackTraceFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- stackTraceToJson(StackTraceElement[]) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- stage() - 类 中的方法org.apache.spark.scheduler.AskPermissionToCommitOutput
-
- STAGE() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- STAGE_DAG() - 类 中的静态方法org.apache.spark.ui.ToolTips
-
- STAGE_TIMELINE() - 类 中的静态方法org.apache.spark.ui.ToolTips
-
- stageAttempt() - 类 中的方法org.apache.spark.scheduler.AskPermissionToCommitOutput
-
- stageAttemptId() - 类 中的方法org.apache.spark.ContextBarrierId
-
- stageAttemptId() - 类 中的方法org.apache.spark.scheduler.SparkListenerExecutorBlacklistedForStage
-
- stageAttemptId() - 类 中的方法org.apache.spark.scheduler.SparkListenerNodeBlacklistedForStage
-
- stageAttemptId() - 类 中的方法org.apache.spark.scheduler.SparkListenerSpeculativeTaskSubmitted
-
- stageAttemptId() - 类 中的方法org.apache.spark.scheduler.SparkListenerStageExecutorMetrics
-
- stageAttemptId() - 类 中的方法org.apache.spark.scheduler.SparkListenerTaskEnd
-
- stageAttemptId() - 类 中的方法org.apache.spark.scheduler.SparkListenerTaskStart
-
- stageAttemptNumber() - 类 中的方法org.apache.spark.BarrierTaskContext
-
- stageAttemptNumber() - 类 中的方法org.apache.spark.TaskContext
-
How many times the stage that this task belongs to has been attempted.
- stageCompletedFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- stageCompletedToJson(SparkListenerStageCompleted) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- stageCreate(Identifier, StructType, Transform[], Map<String, String>) - 接口 中的方法org.apache.spark.sql.connector.catalog.StagingTableCatalog
-
Stage the creation of a table, preparing it to be committed into the metastore.
- stageCreateOrReplace(Identifier, StructType, Transform[], Map<String, String>) - 接口 中的方法org.apache.spark.sql.connector.catalog.StagingTableCatalog
-
- StageData - org.apache.spark.status.api.v1中的类
-
- StagedTable - org.apache.spark.sql.connector.catalog中的接口
-
Represents a table which is staged for being committed to the metastore.
- stageExecutorMetricsFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- stageExecutorMetricsToJson(SparkListenerStageExecutorMetrics) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- stageFailed(String) - 类 中的方法org.apache.spark.scheduler.StageInfo
-
- stageId() - 类 中的方法org.apache.spark.BarrierTaskContext
-
- stageId() - 类 中的方法org.apache.spark.ContextBarrierId
-
- stageId() - 接口 中的方法org.apache.spark.scheduler.Schedulable
-
- stageId() - 类 中的方法org.apache.spark.scheduler.SparkListenerExecutorBlacklistedForStage
-
- stageId() - 类 中的方法org.apache.spark.scheduler.SparkListenerNodeBlacklistedForStage
-
- stageId() - 类 中的方法org.apache.spark.scheduler.SparkListenerSpeculativeTaskSubmitted
-
- stageId() - 类 中的方法org.apache.spark.scheduler.SparkListenerStageExecutorMetrics
-
- stageId() - 类 中的方法org.apache.spark.scheduler.SparkListenerTaskEnd
-
- stageId() - 类 中的方法org.apache.spark.scheduler.SparkListenerTaskStart
-
- stageId() - 类 中的方法org.apache.spark.scheduler.StageInfo
-
- stageId() - 接口 中的方法org.apache.spark.SparkStageInfo
-
- stageId() - 类 中的方法org.apache.spark.SparkStageInfoImpl
-
- stageId() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- stageId() - 类 中的方法org.apache.spark.TaskContext
-
The ID of the stage that this task belong to.
- stageIds() - 类 中的方法org.apache.spark.scheduler.SparkListenerJobStart
-
- stageIds() - 接口 中的方法org.apache.spark.SparkJobInfo
-
- stageIds() - 类 中的方法org.apache.spark.SparkJobInfoImpl
-
- stageIds() - 类 中的方法org.apache.spark.status.api.v1.JobData
-
- stageIds() - 类 中的方法org.apache.spark.status.LiveJob
-
- stageIds() - 类 中的方法org.apache.spark.status.SchedulerPool
-
- stageInfo() - 类 中的方法org.apache.spark.scheduler.SparkListenerStageCompleted
-
- stageInfo() - 类 中的方法org.apache.spark.scheduler.SparkListenerStageSubmitted
-
- StageInfo - org.apache.spark.scheduler中的类
-
:: DeveloperApi ::
Stores information about a stage to pass from the scheduler to SparkListeners.
- StageInfo(int, int, String, int, Seq<RDDInfo>, Seq<Object>, String, TaskMetrics, Seq<Seq<TaskLocation>>, Option<Object>) - 类 的构造器org.apache.spark.scheduler.StageInfo
-
- stageInfoFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
--------------------------------------------------------------------- *
JSON deserialization methods for classes SparkListenerEvents depend on |
- stageInfos() - 类 中的方法org.apache.spark.scheduler.SparkListenerJobStart
-
- stageInfoToJson(StageInfo) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
------------------------------------------------------------------- *
JSON serialization methods for classes SparkListenerEvents depend on |
- stageName() - 类 中的方法org.apache.spark.ml.clustering.InternalKMeansModelWriter
-
- stageName() - 类 中的方法org.apache.spark.ml.clustering.PMMLKMeansModelWriter
-
- stageName() - 类 中的方法org.apache.spark.ml.regression.InternalLinearRegressionModelWriter
-
- stageName() - 类 中的方法org.apache.spark.ml.regression.PMMLLinearRegressionModelWriter
-
- stageName() - 接口 中的方法org.apache.spark.ml.util.MLFormatRegister
-
The string that represents the stage type that this writer supports.
- stageReplace(Identifier, StructType, Transform[], Map<String, String>) - 接口 中的方法org.apache.spark.sql.connector.catalog.StagingTableCatalog
-
- stages() - 类 中的方法org.apache.spark.ml.Pipeline
-
param for pipeline stages
- stages() - 类 中的方法org.apache.spark.ml.PipelineModel
-
- StageStatus - org.apache.spark.status.api.v1中的枚举
-
- stageSubmittedFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- stageSubmittedToJson(SparkListenerStageSubmitted) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- StagingTableCatalog - org.apache.spark.sql.connector.catalog中的接口
-
An optional mix-in for implementations of
TableCatalog that support staging creation of
the a table before committing the table's metadata along with its contents in CREATE TABLE AS
SELECT or REPLACE TABLE AS SELECT operations.
- standardization() - 类 中的方法org.apache.spark.ml.classification.LinearSVC
-
- standardization() - 类 中的方法org.apache.spark.ml.classification.LinearSVCModel
-
- standardization() - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
- standardization() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- standardization() - 接口 中的方法org.apache.spark.ml.param.shared.HasStandardization
-
Param for whether to standardize the training features before fitting the model.
- standardization() - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
- standardization() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionModel
-
- StandardNormalGenerator - org.apache.spark.mllib.random中的类
-
:: DeveloperApi ::
Generates i.i.d. samples from the standard normal distribution.
- StandardNormalGenerator() - 类 的构造器org.apache.spark.mllib.random.StandardNormalGenerator
-
- StandardScaler - org.apache.spark.ml.feature中的类
-
Standardizes features by removing the mean and scaling to unit variance using column summary
statistics on the samples in the training set.
- StandardScaler(String) - 类 的构造器org.apache.spark.ml.feature.StandardScaler
-
- StandardScaler() - 类 的构造器org.apache.spark.ml.feature.StandardScaler
-
- StandardScaler - org.apache.spark.mllib.feature中的类
-
Standardizes features by removing the mean and scaling to unit std using column summary
statistics on the samples in the training set.
- StandardScaler(boolean, boolean) - 类 的构造器org.apache.spark.mllib.feature.StandardScaler
-
- StandardScaler() - 类 的构造器org.apache.spark.mllib.feature.StandardScaler
-
- StandardScalerModel - org.apache.spark.ml.feature中的类
-
- StandardScalerModel - org.apache.spark.mllib.feature中的类
-
Represents a StandardScaler model that can transform vectors.
- StandardScalerModel(Vector, Vector, boolean, boolean) - 类 的构造器org.apache.spark.mllib.feature.StandardScalerModel
-
- StandardScalerModel(Vector, Vector) - 类 的构造器org.apache.spark.mllib.feature.StandardScalerModel
-
- StandardScalerModel(Vector) - 类 的构造器org.apache.spark.mllib.feature.StandardScalerModel
-
- StandardScalerParams - org.apache.spark.ml.feature中的接口
-
- starGraph(SparkContext, int) - 类 中的静态方法org.apache.spark.graphx.util.GraphGenerators
-
Create a star graph with vertex 0 being the center.
- start() - 接口 中的方法org.apache.spark.metrics.sink.Sink
-
- start() - 接口 中的方法org.apache.spark.scheduler.SchedulerBackend
-
- start() - 接口 中的方法org.apache.spark.scheduler.TaskScheduler
-
- start(String) - 类 中的方法org.apache.spark.sql.streaming.DataStreamWriter
-
Starts the execution of the streaming query, which will continually output results to the given
path as new data arrives.
- start() - 类 中的方法org.apache.spark.sql.streaming.DataStreamWriter
-
Starts the execution of the streaming query, which will continually output results to the given
path as new data arrives.
- start() - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Start the execution of the streams.
- start() - 类 中的方法org.apache.spark.streaming.dstream.ConstantInputDStream
-
- start() - 类 中的方法org.apache.spark.streaming.dstream.InputDStream
-
Method called to start receiving data.
- start() - 类 中的方法org.apache.spark.streaming.dstream.ReceiverInputDStream
-
- start() - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Start the execution of the streams.
- startApplication(SparkAppHandle.Listener...) - 类 中的方法org.apache.spark.launcher.AbstractLauncher
-
Starts a Spark application.
- startApplication(SparkAppHandle.Listener...) - 类 中的方法org.apache.spark.launcher.InProcessLauncher
-
Starts a Spark application.
- startApplication(SparkAppHandle.Listener...) - 类 中的方法org.apache.spark.launcher.SparkLauncher
-
Starts a Spark application.
- startIndexInLevel(int) - 类 中的静态方法org.apache.spark.mllib.tree.model.Node
-
Return the index of the first node in the given level.
- startJettyServer(String, int, org.apache.spark.SSLOptions, SparkConf, String) - 类 中的静态方法org.apache.spark.ui.JettyUtils
-
Attempt to start a Jetty server bound to the supplied hostName:port using the given
context handlers.
- startOffset() - 类 中的方法org.apache.spark.sql.streaming.SourceProgress
-
- startOffset() - 异常错误 中的方法org.apache.spark.sql.streaming.StreamingQueryException
-
- startPosition() - 异常错误 中的方法org.apache.spark.sql.AnalysisException
-
- startReduceId() - 类 中的方法org.apache.spark.storage.ShuffleBlockBatchId
-
- startServiceOnPort(int, Function1<Object, Tuple2<T, Object>>, SparkConf, String) - 类 中的静态方法org.apache.spark.util.Utils
-
Attempt to start a service on the given port, or fail after a number of attempts.
- startsWith(Column) - 类 中的方法org.apache.spark.sql.Column
-
String starts with.
- startsWith(String) - 类 中的方法org.apache.spark.sql.Column
-
String starts with another string literal.
- startTime() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
- startTime() - 类 中的方法org.apache.spark.SparkContext
-
- startTime() - 类 中的方法org.apache.spark.status.api.v1.ApplicationAttemptInfo
-
- startTime() - 类 中的方法org.apache.spark.status.api.v1.streaming.OutputOperationInfo
-
- startTime() - 类 中的方法org.apache.spark.status.api.v1.streaming.StreamingStatistics
-
- startTime() - 类 中的方法org.apache.spark.streaming.scheduler.OutputOperationInfo
-
- stat() - 类 中的方法org.apache.spark.sql.Dataset
-
- StatCounter - org.apache.spark.util中的类
-
A class for tracking the statistics of a set of numbers (count, mean and variance) in a
numerically robust way.
- StatCounter(TraversableOnce<Object>) - 类 的构造器org.apache.spark.util.StatCounter
-
- StatCounter() - 类 的构造器org.apache.spark.util.StatCounter
-
Initialize the StatCounter with no values.
- state() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.StatusUpdate
-
- state() - 类 中的方法org.apache.spark.scheduler.local.StatusUpdate
-
- State<S> - org.apache.spark.streaming中的类
-
:: Experimental ::
Abstract class for getting and updating the state in mapping function used in the
mapWithState
operation of a
pair DStream (Scala)
or a
JavaPairDStream (Java).
- State() - 类 的构造器org.apache.spark.streaming.State
-
- stateChanged(SparkAppHandle) - 接口 中的方法org.apache.spark.launcher.SparkAppHandle.Listener
-
Callback for changes in the handle's state.
- statement() - 类 中的方法org.apache.spark.ml.feature.SQLTransformer
-
SQL statement parameter.
- StateOperatorProgress - org.apache.spark.sql.streaming中的类
-
Information about updates made to stateful operators in a
StreamingQuery during a trigger.
- stateOperators() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryProgress
-
- stateSnapshots() - 类 中的方法org.apache.spark.streaming.api.java.JavaMapWithStateDStream
-
- stateSnapshots() - 类 中的方法org.apache.spark.streaming.dstream.MapWithStateDStream
-
Return a pair DStream where each RDD is the snapshot of the state of all the keys.
- StateSpec<KeyType,ValueType,StateType,MappedType> - org.apache.spark.streaming中的类
-
:: Experimental ::
Abstract class representing all the specifications of the DStream transformation
mapWithState operation of a
pair DStream (Scala) or a
JavaPairDStream (Java).
- StateSpec() - 类 的构造器org.apache.spark.streaming.StateSpec
-
- staticPageRank(int, double) - 类 中的方法org.apache.spark.graphx.GraphOps
-
Run PageRank for a fixed number of iterations returning a graph with vertex attributes
containing the PageRank and edge attributes the normalized edge weight.
- staticParallelPersonalizedPageRank(long[], int, double) - 类 中的方法org.apache.spark.graphx.GraphOps
-
Run parallel personalized PageRank for a given array of source vertices, such
that all random walks are started relative to the source vertices
- staticPersonalizedPageRank(long, int, double) - 类 中的方法org.apache.spark.graphx.GraphOps
-
Run Personalized PageRank for a fixed number of iterations with
with all iterations originating at the source node
returning a graph with vertex attributes
containing the PageRank and edge attributes the normalized edge weight.
- StaticSources - org.apache.spark.metrics.source中的类
-
- StaticSources() - 类 的构造器org.apache.spark.metrics.source.StaticSources
-
- statistic() - 类 中的方法org.apache.spark.mllib.stat.test.ChiSqTestResult
-
- statistic() - 类 中的方法org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult
-
- statistic() - 接口 中的方法org.apache.spark.mllib.stat.test.TestResult
-
Test statistic.
- Statistics - org.apache.spark.mllib.stat中的类
-
API for statistical functions in MLlib.
- Statistics() - 类 的构造器org.apache.spark.mllib.stat.Statistics
-
- Statistics - org.apache.spark.sql.connector.read中的接口
-
- stats() - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Return a
StatCounter object that captures the mean, variance and
count of the RDD's elements in one operation.
- stats() - 类 中的方法org.apache.spark.mllib.tree.model.Node
-
- stats() - 类 中的方法org.apache.spark.rdd.DoubleRDDFunctions
-
Return a
StatCounter object that captures the mean, variance and
count of the RDD's elements in one operation.
- StatsdMetricType - org.apache.spark.metrics.sink中的类
-
- StatsdMetricType() - 类 的构造器org.apache.spark.metrics.sink.StatsdMetricType
-
- StatsReportListener - org.apache.spark.scheduler中的类
-
:: DeveloperApi ::
Simple SparkListener that logs a few summary statistics when each stage completes.
- StatsReportListener() - 类 的构造器org.apache.spark.scheduler.StatsReportListener
-
- StatsReportListener - org.apache.spark.streaming.scheduler中的类
-
:: DeveloperApi ::
A simple StreamingListener that logs summary statistics across Spark Streaming batches
param: numBatchInfos Number of last batches to consider for generating statistics (default: 10)
- StatsReportListener(int) - 类 的构造器org.apache.spark.streaming.scheduler.StatsReportListener
-
- Status - org.apache.spark.internal.config中的类
-
- Status() - 类 的构造器org.apache.spark.internal.config.Status
-
- status() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
- status() - 接口 中的方法org.apache.spark.SparkJobInfo
-
- status() - 类 中的方法org.apache.spark.SparkJobInfoImpl
-
- status() - 接口 中的方法org.apache.spark.sql.streaming.StreamingQuery
-
Returns the current status of the query.
- status() - 类 中的方法org.apache.spark.status.api.v1.JobData
-
- status() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- status() - 类 中的方法org.apache.spark.status.api.v1.streaming.BatchInfo
-
- status() - 类 中的方法org.apache.spark.status.api.v1.TaskData
-
- status() - 类 中的方法org.apache.spark.status.LiveJob
-
- status() - 类 中的方法org.apache.spark.status.LiveStage
-
- STATUS() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- status() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.BlockLocationsAndStatus
-
- statusTracker() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
- statusTracker() - 类 中的方法org.apache.spark.SparkContext
-
- StatusUpdate(String, long, Enumeration.Value, org.apache.spark.util.SerializableBuffer, Map<String, ResourceInformation>) - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.StatusUpdate
-
- StatusUpdate - org.apache.spark.scheduler.local中的类
-
- StatusUpdate(long, Enumeration.Value, ByteBuffer) - 类 的构造器org.apache.spark.scheduler.local.StatusUpdate
-
- StatusUpdate$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.StatusUpdate$
-
- STD() - 类 中的静态方法org.apache.spark.ml.attribute.AttributeKeys
-
- std() - 类 中的方法org.apache.spark.ml.attribute.NumericAttribute
-
- std() - 类 中的方法org.apache.spark.ml.feature.StandardScalerModel
-
- std() - 类 中的方法org.apache.spark.mllib.feature.StandardScalerModel
-
- std() - 类 中的方法org.apache.spark.mllib.random.LogNormalGenerator
-
- stddev(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: alias for stddev_samp.
- stddev(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: alias for stddev_samp.
- stddev_pop(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the population standard deviation of
the expression in a group.
- stddev_pop(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the population standard deviation of
the expression in a group.
- stddev_samp(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the sample standard deviation of
the expression in a group.
- stddev_samp(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the sample standard deviation of
the expression in a group.
- stdev() - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Compute the population standard deviation of this RDD's elements.
- stdev() - 类 中的方法org.apache.spark.rdd.DoubleRDDFunctions
-
Compute the population standard deviation of this RDD's elements.
- stdev() - 类 中的方法org.apache.spark.util.StatCounter
-
Return the population standard deviation of the values.
- stepSize() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- stepSize() - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- stepSize() - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
- stepSize() - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- stepSize() - 类 中的方法org.apache.spark.ml.feature.Word2VecModel
-
- stepSize() - 接口 中的方法org.apache.spark.ml.param.shared.HasStepSize
-
Param for Step size to be used for each iteration of optimization (> 0).
- stepSize() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- stepSize() - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- stepSize() - 接口 中的方法org.apache.spark.ml.tree.GBTParams
-
Param for Step size (a.k.a. learning rate) in interval (0, 1] for shrinking
the contribution of each estimator.
- stop() - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Shut down the SparkContext.
- stop() - 接口 中的方法org.apache.spark.broadcast.BroadcastFactory
-
- stop() - 接口 中的方法org.apache.spark.launcher.SparkAppHandle
-
Asks the application to stop.
- stop() - 接口 中的方法org.apache.spark.metrics.sink.Sink
-
- stop() - 类 中的方法org.apache.spark.rpc.netty.MessageLoop
-
- stop() - 接口 中的方法org.apache.spark.rpc.RpcEndpoint
-
- stop() - 接口 中的方法org.apache.spark.scheduler.SchedulerBackend
-
- stop() - 接口 中的方法org.apache.spark.scheduler.TaskScheduler
-
- stop() - 类 中的方法org.apache.spark.SparkContext
-
Shut down the SparkContext.
- stop() - 接口 中的方法org.apache.spark.sql.connector.read.streaming.SparkDataStream
-
Stop this source and free any resources it has allocated.
- stop() - 类 中的方法org.apache.spark.sql.SparkSession
-
Stop the underlying SparkContext.
- stop() - 接口 中的方法org.apache.spark.sql.streaming.StreamingQuery
-
Stops the execution of this query if it is running.
- stop() - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Stop the execution of the streams.
- stop(boolean) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Stop the execution of the streams.
- stop(boolean, boolean) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Stop the execution of the streams.
- stop() - 类 中的方法org.apache.spark.streaming.dstream.ConstantInputDStream
-
- stop() - 类 中的方法org.apache.spark.streaming.dstream.InputDStream
-
Method called to stop receiving data.
- stop() - 类 中的方法org.apache.spark.streaming.dstream.ReceiverInputDStream
-
- stop(String) - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Stop the receiver completely.
- stop(String, Throwable) - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Stop the receiver completely due to an exception
- stop(boolean) - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Stop the execution of the streams immediately (does not wait for all received data
to be processed).
- stop(boolean, boolean) - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Stop the execution of the streams, with option of ensuring all received data
has been processed.
- StopAllReceivers - org.apache.spark.streaming.scheduler中的类
-
This message will trigger ReceiverTrackerEndpoint to send stop signals to all registered
receivers.
- StopAllReceivers() - 类 的构造器org.apache.spark.streaming.scheduler.StopAllReceivers
-
- StopBlockManagerMaster$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.StopBlockManagerMaster$
-
- StopCoordinator - org.apache.spark.scheduler中的类
-
- StopCoordinator() - 类 的构造器org.apache.spark.scheduler.StopCoordinator
-
- StopDriver$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.StopDriver$
-
- StopExecutor - org.apache.spark.scheduler.local中的类
-
- StopExecutor() - 类 的构造器org.apache.spark.scheduler.local.StopExecutor
-
- StopExecutor$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.StopExecutor$
-
- StopExecutors$() - 类 的构造器org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.StopExecutors$
-
- StopMapOutputTracker - org.apache.spark中的类
-
- StopMapOutputTracker() - 类 的构造器org.apache.spark.StopMapOutputTracker
-
- StopReceiver - org.apache.spark.streaming.receiver中的类
-
- StopReceiver() - 类 的构造器org.apache.spark.streaming.receiver.StopReceiver
-
- stopWords() - 类 中的方法org.apache.spark.ml.feature.StopWordsRemover
-
The words to be filtered out.
- StopWordsRemover - org.apache.spark.ml.feature中的类
-
A feature transformer that filters out stop words from input.
- StopWordsRemover(String) - 类 的构造器org.apache.spark.ml.feature.StopWordsRemover
-
- StopWordsRemover() - 类 的构造器org.apache.spark.ml.feature.StopWordsRemover
-
- storage() - 类 中的方法org.apache.spark.sql.hive.execution.InsertIntoHiveDirCommand
-
- STORAGE_LEVEL() - 类 中的静态方法org.apache.spark.ui.storage.ToolTips
-
- STORAGE_MEMORY() - 类 中的静态方法org.apache.spark.ui.ToolTips
-
- storageLevel() - 类 中的方法org.apache.spark.sql.Dataset
-
Get the Dataset's current storage level, or StorageLevel.NONE if not persisted.
- storageLevel() - 类 中的方法org.apache.spark.status.api.v1.RDDPartitionInfo
-
- storageLevel() - 类 中的方法org.apache.spark.status.api.v1.RDDStorageInfo
-
- storageLevel() - 类 中的方法org.apache.spark.storage.BlockManagerMessages.UpdateBlockInfo
-
- storageLevel() - 类 中的方法org.apache.spark.storage.BlockStatus
-
- storageLevel() - 类 中的方法org.apache.spark.storage.BlockUpdatedInfo
-
- storageLevel() - 类 中的方法org.apache.spark.storage.RDDInfo
-
- StorageLevel - org.apache.spark.storage中的类
-
:: DeveloperApi ::
Flags for controlling the storage of an RDD.
- StorageLevel() - 类 的构造器org.apache.spark.storage.StorageLevel
-
- storageLevel() - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
- storageLevelFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- StorageLevels - org.apache.spark.api.java中的类
-
Expose some commonly useful storage level constants.
- StorageLevels() - 类 的构造器org.apache.spark.api.java.StorageLevels
-
- storageLevelToJson(StorageLevel) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- StorageUtils - org.apache.spark.storage中的类
-
Helper methods for storage-related objects.
- StorageUtils() - 类 的构造器org.apache.spark.storage.StorageUtils
-
- store(T) - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Store a single item of received data to Spark's memory.
- store(ArrayBuffer<T>) - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Store an ArrayBuffer of received data as a data block into Spark's memory.
- store(ArrayBuffer<T>, Object) - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Store an ArrayBuffer of received data as a data block into Spark's memory.
- store(Iterator<T>) - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Store an iterator of received data as a data block into Spark's memory.
- store(Iterator<T>, Object) - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Store an iterator of received data as a data block into Spark's memory.
- store(Iterator<T>) - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Store an iterator of received data as a data block into Spark's memory.
- store(Iterator<T>, Object) - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Store an iterator of received data as a data block into Spark's memory.
- store(ByteBuffer) - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Store the bytes of received data as a data block into Spark's memory.
- store(ByteBuffer, Object) - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Store the bytes of received data as a data block into Spark's memory.
- storeBlock(StreamBlockId, ReceivedBlock) - 接口 中的方法org.apache.spark.streaming.receiver.ReceivedBlockHandler
-
Store a received block with the given block id and return related metadata
- storeValue(T) - 类 中的方法org.apache.spark.storage.memory.DeserializedValuesHolder
-
- storeValue(T) - 类 中的方法org.apache.spark.storage.memory.SerializedValuesHolder
-
- storeValue(T) - 接口 中的方法org.apache.spark.storage.memory.ValuesHolder
-
- strategy() - 类 中的方法org.apache.spark.ml.feature.Imputer
-
- strategy() - 类 中的方法org.apache.spark.ml.feature.ImputerModel
-
- strategy() - 接口 中的方法org.apache.spark.ml.feature.ImputerParams
-
The imputation strategy.
- Strategy - org.apache.spark.mllib.tree.configuration中的类
-
Stores all the configuration options for tree construction
param: algo Learning goal.
- Strategy(Enumeration.Value, Impurity, int, int, int, Enumeration.Value, Map<Object, Object>, int, double, int, double, boolean, int, double) - 类 的构造器org.apache.spark.mllib.tree.configuration.Strategy
-
- Strategy(Enumeration.Value, Impurity, int, int, int, Enumeration.Value, Map<Object, Object>, int, double, int, double, boolean, int) - 类 的构造器org.apache.spark.mllib.tree.configuration.Strategy
-
Backwards compatible constructor for
Strategy
- Strategy(Enumeration.Value, Impurity, int, int, int, Map<Integer, Integer>) - 类 的构造器org.apache.spark.mllib.tree.configuration.Strategy
-
- StratifiedSamplingUtils - org.apache.spark.util.random中的类
-
Auxiliary functions and data structures for the sampleByKey method in PairRDDFunctions.
- StratifiedSamplingUtils() - 类 的构造器org.apache.spark.util.random.StratifiedSamplingUtils
-
- STREAM() - 类 中的静态方法org.apache.spark.storage.BlockId
-
- StreamBlockId - org.apache.spark.storage中的类
-
- StreamBlockId(int, long) - 类 的构造器org.apache.spark.storage.StreamBlockId
-
- streamId() - 类 中的方法org.apache.spark.status.api.v1.streaming.ReceiverInfo
-
- streamId() - 类 中的方法org.apache.spark.storage.StreamBlockId
-
- streamId() - 类 中的方法org.apache.spark.streaming.receiver.Receiver
-
Get the unique identifier the receiver input stream that this
receiver is associated with.
- streamId() - 类 中的方法org.apache.spark.streaming.scheduler.ReceiverInfo
-
- streamIdToInputInfo() - 类 中的方法org.apache.spark.streaming.scheduler.BatchInfo
-
- Streaming - org.apache.spark.internal.config中的类
-
- Streaming() - 类 的构造器org.apache.spark.internal.config.Streaming
-
- StreamingContext - org.apache.spark.streaming中的类
-
Main entry point for Spark Streaming functionality.
- StreamingContext(SparkContext, Duration) - 类 的构造器org.apache.spark.streaming.StreamingContext
-
Create a StreamingContext using an existing SparkContext.
- StreamingContext(SparkConf, Duration) - 类 的构造器org.apache.spark.streaming.StreamingContext
-
Create a StreamingContext by providing the configuration necessary for a new SparkContext.
- StreamingContext(String, String, Duration, String, Seq<String>, Map<String, String>) - 类 的构造器org.apache.spark.streaming.StreamingContext
-
Create a StreamingContext by providing the details necessary for creating a new SparkContext.
- StreamingContext(String, Configuration) - 类 的构造器org.apache.spark.streaming.StreamingContext
-
Recreate a StreamingContext from a checkpoint file.
- StreamingContext(String) - 类 的构造器org.apache.spark.streaming.StreamingContext
-
Recreate a StreamingContext from a checkpoint file.
- StreamingContext(String, SparkContext) - 类 的构造器org.apache.spark.streaming.StreamingContext
-
Recreate a StreamingContext from a checkpoint file using an existing SparkContext.
- StreamingContextPythonHelper - org.apache.spark.streaming中的类
-
- StreamingContextPythonHelper() - 类 的构造器org.apache.spark.streaming.StreamingContextPythonHelper
-
- StreamingContextState - org.apache.spark.streaming中的枚举
-
:: DeveloperApi ::
Represents the state of a StreamingContext.
- StreamingDataWriterFactory - org.apache.spark.sql.connector.write.streaming中的接口
-
- StreamingKMeans - org.apache.spark.mllib.clustering中的类
-
StreamingKMeans provides methods for configuring a
streaming k-means analysis, training the model on streaming,
and using the model to make predictions on streaming data.
- StreamingKMeans(int, double, String) - 类 的构造器org.apache.spark.mllib.clustering.StreamingKMeans
-
- StreamingKMeans() - 类 的构造器org.apache.spark.mllib.clustering.StreamingKMeans
-
- StreamingKMeansModel - org.apache.spark.mllib.clustering中的类
-
StreamingKMeansModel extends MLlib's KMeansModel for streaming
algorithms, so it can keep track of a continuously updated weight
associated with each cluster, and also update the model by
doing a single iteration of the standard k-means algorithm.
- StreamingKMeansModel(Vector[], double[]) - 类 的构造器org.apache.spark.mllib.clustering.StreamingKMeansModel
-
- StreamingLinearAlgorithm<M extends GeneralizedLinearModel,A extends GeneralizedLinearAlgorithm<M>> - org.apache.spark.mllib.regression中的类
-
:: DeveloperApi ::
StreamingLinearAlgorithm implements methods for continuously
training a generalized linear model on streaming data,
and using it for prediction on (possibly different) streaming data.
- StreamingLinearAlgorithm() - 类 的构造器org.apache.spark.mllib.regression.StreamingLinearAlgorithm
-
- StreamingLinearRegressionWithSGD - org.apache.spark.mllib.regression中的类
-
Train or predict a linear regression model on streaming data.
- StreamingLinearRegressionWithSGD() - 类 的构造器org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
-
Construct a StreamingLinearRegression object with default parameters:
{stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0}.
- StreamingListener - org.apache.spark.streaming.scheduler中的接口
-
:: DeveloperApi ::
A listener interface for receiving information about an ongoing streaming
computation.
- StreamingListenerBatchCompleted - org.apache.spark.streaming.scheduler中的类
-
- StreamingListenerBatchCompleted(BatchInfo) - 类 的构造器org.apache.spark.streaming.scheduler.StreamingListenerBatchCompleted
-
- StreamingListenerBatchStarted - org.apache.spark.streaming.scheduler中的类
-
- StreamingListenerBatchStarted(BatchInfo) - 类 的构造器org.apache.spark.streaming.scheduler.StreamingListenerBatchStarted
-
- StreamingListenerBatchSubmitted - org.apache.spark.streaming.scheduler中的类
-
- StreamingListenerBatchSubmitted(BatchInfo) - 类 的构造器org.apache.spark.streaming.scheduler.StreamingListenerBatchSubmitted
-
- StreamingListenerEvent - org.apache.spark.streaming.scheduler中的接口
-
:: DeveloperApi ::
Base trait for events related to StreamingListener
- StreamingListenerOutputOperationCompleted - org.apache.spark.streaming.scheduler中的类
-
- StreamingListenerOutputOperationCompleted(OutputOperationInfo) - 类 的构造器org.apache.spark.streaming.scheduler.StreamingListenerOutputOperationCompleted
-
- StreamingListenerOutputOperationStarted - org.apache.spark.streaming.scheduler中的类
-
- StreamingListenerOutputOperationStarted(OutputOperationInfo) - 类 的构造器org.apache.spark.streaming.scheduler.StreamingListenerOutputOperationStarted
-
- StreamingListenerReceiverError - org.apache.spark.streaming.scheduler中的类
-
- StreamingListenerReceiverError(ReceiverInfo) - 类 的构造器org.apache.spark.streaming.scheduler.StreamingListenerReceiverError
-
- StreamingListenerReceiverStarted - org.apache.spark.streaming.scheduler中的类
-
- StreamingListenerReceiverStarted(ReceiverInfo) - 类 的构造器org.apache.spark.streaming.scheduler.StreamingListenerReceiverStarted
-
- StreamingListenerReceiverStopped - org.apache.spark.streaming.scheduler中的类
-
- StreamingListenerReceiverStopped(ReceiverInfo) - 类 的构造器org.apache.spark.streaming.scheduler.StreamingListenerReceiverStopped
-
- StreamingListenerStreamingStarted - org.apache.spark.streaming.scheduler中的类
-
- StreamingListenerStreamingStarted(long) - 类 的构造器org.apache.spark.streaming.scheduler.StreamingListenerStreamingStarted
-
- StreamingLogisticRegressionWithSGD - org.apache.spark.mllib.classification中的类
-
Train or predict a logistic regression model on streaming data.
- StreamingLogisticRegressionWithSGD() - 类 的构造器org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD
-
Construct a StreamingLogisticRegression object with default parameters:
{stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0, regParam: 0.0}.
- StreamingQuery - org.apache.spark.sql.streaming中的接口
-
A handle to a query that is executing continuously in the background as new data arrives.
- StreamingQueryException - org.apache.spark.sql.streaming中的异常错误
-
- StreamingQueryListener - org.apache.spark.sql.streaming中的类
-
- StreamingQueryListener() - 类 的构造器org.apache.spark.sql.streaming.StreamingQueryListener
-
- StreamingQueryListener.Event - org.apache.spark.sql.streaming中的接口
-
- StreamingQueryListener.QueryProgressEvent - org.apache.spark.sql.streaming中的类
-
Event representing any progress updates in a query.
- StreamingQueryListener.QueryStartedEvent - org.apache.spark.sql.streaming中的类
-
Event representing the start of a query
param: id A unique query id that persists across restarts.
- StreamingQueryListener.QueryTerminatedEvent - org.apache.spark.sql.streaming中的类
-
Event representing that termination of a query.
- StreamingQueryManager - org.apache.spark.sql.streaming中的类
-
- StreamingQueryProgress - org.apache.spark.sql.streaming中的类
-
Information about progress made in the execution of a
StreamingQuery during
a trigger.
- StreamingQueryStatus - org.apache.spark.sql.streaming中的类
-
Reports information about the instantaneous status of a streaming query.
- StreamingStatistics - org.apache.spark.status.api.v1.streaming中的类
-
- StreamingTest - org.apache.spark.mllib.stat.test中的类
-
Performs online 2-sample significance testing for a stream of (Boolean, Double) pairs.
- StreamingTest() - 类 的构造器org.apache.spark.mllib.stat.test.StreamingTest
-
- StreamingTestMethod - org.apache.spark.mllib.stat.test中的接口
-
- StreamingWrite - org.apache.spark.sql.connector.write.streaming中的接口
-
An interface that defines how to write the data to data source in streaming queries.
- StreamInputInfo - org.apache.spark.streaming.scheduler中的类
-
:: DeveloperApi ::
Track the information of input stream at specified batch time.
- StreamInputInfo(int, long, Map<String, Object>) - 类 的构造器org.apache.spark.streaming.scheduler.StreamInputInfo
-
- streamName() - 类 中的方法org.apache.spark.status.api.v1.streaming.ReceiverInfo
-
- streams() - 类 中的方法org.apache.spark.sql.SparkSession
-
Returns a StreamingQueryManager that allows managing all the
StreamingQuerys active on this.
- streams() - 类 中的方法org.apache.spark.sql.SQLContext
-
Returns a
StreamingQueryManager that allows managing all the
StreamingQueries active on
this context.
- StreamSinkProvider - org.apache.spark.sql.sources中的接口
-
::Experimental::
Implemented by objects that can produce a streaming Sink for a specific format or system.
- StreamSourceProvider - org.apache.spark.sql.sources中的接口
-
::Experimental::
Implemented by objects that can produce a streaming Source for a specific format or system.
- STRING() - 类 中的静态方法org.apache.spark.api.r.SerializationFormats
-
- string() - 类 中的方法org.apache.spark.sql.ColumnName
-
Creates a new StructField of type string.
- STRING() - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for nullable string type.
- StringArrayParam - org.apache.spark.ml.param中的类
-
:: DeveloperApi ::
Specialized version of Param[Array[String} for Java.
- StringArrayParam(Params, String, String, Function1<String[], Object>) - 类 的构造器org.apache.spark.ml.param.StringArrayParam
-
- StringArrayParam(Params, String, String) - 类 的构造器org.apache.spark.ml.param.StringArrayParam
-
- StringContains - org.apache.spark.sql.sources中的类
-
A filter that evaluates to true iff the attribute evaluates to
a string that contains the string value.
- StringContains(String, String) - 类 的构造器org.apache.spark.sql.sources.StringContains
-
- StringEndsWith - org.apache.spark.sql.sources中的类
-
A filter that evaluates to true iff the attribute evaluates to
a string that ends with value.
- StringEndsWith(String, String) - 类 的构造器org.apache.spark.sql.sources.StringEndsWith
-
- stringHalfWidth(String) - 类 中的静态方法org.apache.spark.util.Utils
-
Return the number of half widths in a given string.
- StringIndexer - org.apache.spark.ml.feature中的类
-
A label indexer that maps string column(s) of labels to ML column(s) of label indices.
- StringIndexer(String) - 类 的构造器org.apache.spark.ml.feature.StringIndexer
-
- StringIndexer() - 类 的构造器org.apache.spark.ml.feature.StringIndexer
-
- StringIndexerAggregator - org.apache.spark.ml.feature中的类
-
A SQL Aggregator used by StringIndexer to count labels in string columns during fitting.
- StringIndexerAggregator(int) - 类 的构造器org.apache.spark.ml.feature.StringIndexerAggregator
-
- StringIndexerBase - org.apache.spark.ml.feature中的接口
-
- StringIndexerModel - org.apache.spark.ml.feature中的类
-
- StringIndexerModel(String, String[][]) - 类 的构造器org.apache.spark.ml.feature.StringIndexerModel
-
- StringIndexerModel(String, String[]) - 类 的构造器org.apache.spark.ml.feature.StringIndexerModel
-
- StringIndexerModel(String[]) - 类 的构造器org.apache.spark.ml.feature.StringIndexerModel
-
- StringIndexerModel(String[][]) - 类 的构造器org.apache.spark.ml.feature.StringIndexerModel
-
- stringIndexerOrderType() - 类 中的方法org.apache.spark.ml.feature.RFormula
-
- stringIndexerOrderType() - 接口 中的方法org.apache.spark.ml.feature.RFormulaBase
-
Param for how to order categories of a string FEATURE column used by StringIndexer.
- stringIndexerOrderType() - 类 中的方法org.apache.spark.ml.feature.RFormulaModel
-
- stringOrderType() - 类 中的方法org.apache.spark.ml.feature.StringIndexer
-
- stringOrderType() - 接口 中的方法org.apache.spark.ml.feature.StringIndexerBase
-
Param for how to order labels of string column.
- stringOrderType() - 类 中的方法org.apache.spark.ml.feature.StringIndexerModel
-
- StringRRDD<T> - org.apache.spark.api.r中的类
-
An RDD that stores R objects as Array[String].
- StringRRDD(RDD<T>, byte[], String, byte[], Object[], ClassTag<T>) - 类 的构造器org.apache.spark.api.r.StringRRDD
-
- StringStartsWith - org.apache.spark.sql.sources中的类
-
A filter that evaluates to true iff the attribute evaluates to
a string that starts with value.
- StringStartsWith(String, String) - 类 的构造器org.apache.spark.sql.sources.StringStartsWith
-
- StringToColumn(StringContext) - 类 的构造器org.apache.spark.sql.SQLImplicits.StringToColumn
-
- stringToSeq(String, Function1<String, T>) - 类 中的静态方法org.apache.spark.internal.config.ConfigHelpers
-
- stringToSeq(String) - 类 中的静态方法org.apache.spark.util.Utils
-
- StringType - 类 中的静态变量org.apache.spark.sql.types.DataTypes
-
Gets the StringType object.
- StringType - org.apache.spark.sql.types中的类
-
The data type representing String values.
- StringType() - 类 的构造器org.apache.spark.sql.types.StringType
-
- stronglyConnectedComponents(int) - 类 中的方法org.apache.spark.graphx.GraphOps
-
Compute the strongly connected component (SCC) of each vertex and return a graph with the
vertex value containing the lowest vertex id in the SCC containing that vertex.
- StronglyConnectedComponents - org.apache.spark.graphx.lib中的类
-
Strongly connected components algorithm implementation.
- StronglyConnectedComponents() - 类 的构造器org.apache.spark.graphx.lib.StronglyConnectedComponents
-
- struct(Seq<StructField>) - 类 中的方法org.apache.spark.sql.ColumnName
-
Creates a new StructField of type struct.
- struct(StructType) - 类 中的方法org.apache.spark.sql.ColumnName
-
Creates a new StructField of type struct.
- struct(Column...) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a new struct column.
- struct(String, String...) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a new struct column that composes multiple input columns.
- struct(Seq<Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a new struct column.
- struct(String, Seq<String>) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a new struct column that composes multiple input columns.
- StructField - org.apache.spark.sql.types中的类
-
A field inside a StructType.
- StructField(String, DataType, boolean, Metadata) - 类 的构造器org.apache.spark.sql.types.StructField
-
- StructType - org.apache.spark.sql.types中的类
-
A
StructType object can be constructed by
StructType(fields: Seq[StructField])
For a
StructType object, one or multiple
StructFields can be extracted by names.
- StructType(StructField[]) - 类 的构造器org.apache.spark.sql.types.StructType
-
- StructType() - 类 的构造器org.apache.spark.sql.types.StructType
-
No-arg constructor for kryo.
- stsCredentials(String, String) - 类 中的方法org.apache.spark.streaming.kinesis.SparkAWSCredentials.Builder
-
Use STS to assume an IAM role for temporary session-based authentication.
- stsCredentials(String, String, String) - 类 中的方法org.apache.spark.streaming.kinesis.SparkAWSCredentials.Builder
-
Use STS to assume an IAM role for temporary session-based authentication.
- StudentTTest - org.apache.spark.mllib.stat.test中的类
-
Performs Students's 2-sample t-test.
- StudentTTest() - 类 的构造器org.apache.spark.mllib.stat.test.StudentTTest
-
- subgraph(Function1<EdgeTriplet<VD, ED>, Object>, Function2<Object, VD, Object>) - 类 中的方法org.apache.spark.graphx.Graph
-
Restricts the graph to only the vertices and edges satisfying the predicates.
- subgraph(Function1<EdgeTriplet<VD, ED>, Object>, Function2<Object, VD, Object>) - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- submissionTime() - 类 中的方法org.apache.spark.scheduler.StageInfo
-
When this stage was submitted from the DAGScheduler to a TaskScheduler.
- submissionTime() - 接口 中的方法org.apache.spark.SparkStageInfo
-
- submissionTime() - 类 中的方法org.apache.spark.SparkStageInfoImpl
-
- submissionTime() - 类 中的方法org.apache.spark.status.api.v1.JobData
-
- submissionTime() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- submissionTime() - 类 中的方法org.apache.spark.status.LiveJob
-
- submissionTime() - 类 中的方法org.apache.spark.streaming.scheduler.BatchInfo
-
- submitJob(RDD<T>, Function1<Iterator<T>, U>, Seq<Object>, Function2<Object, U, BoxedUnit>, Function0<R>) - 接口 中的方法org.apache.spark.JobSubmitter
-
Submit a job for execution and return a FutureAction holding the result.
- submitJob(RDD<T>, Function1<Iterator<T>, U>, Seq<Object>, Function2<Object, U, BoxedUnit>, Function0<R>) - 类 中的方法org.apache.spark.SparkContext
-
Submit a job for execution and return a FutureJob holding the result.
- submitTasks(TaskSet) - 接口 中的方法org.apache.spark.scheduler.TaskScheduler
-
- subModels() - 类 中的方法org.apache.spark.ml.tuning.CrossValidatorModel
-
- subModels() - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplitModel
-
- subsamplingRate() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- subsamplingRate() - 类 中的方法org.apache.spark.ml.classification.GBTClassifier
-
- subsamplingRate() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- subsamplingRate() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassifier
-
- subsamplingRate() - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- subsamplingRate() - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
- subsamplingRate() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
For Online optimizer only: optimizer = "online".
- subsamplingRate() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- subsamplingRate() - 类 中的方法org.apache.spark.ml.regression.GBTRegressor
-
- subsamplingRate() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- subsamplingRate() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressor
-
- subsamplingRate() - 接口 中的方法org.apache.spark.ml.tree.TreeEnsembleParams
-
Fraction of the training data used for learning each decision tree, in range (0, 1].
- subsamplingRate() - 类 中的方法org.apache.spark.mllib.tree.configuration.Strategy
-
- subsetAccuracy() - 类 中的方法org.apache.spark.mllib.evaluation.MultilabelMetrics
-
Returns subset accuracy
(for equal sets of labels)
- substituteAppId(String, String) - 类 中的静态方法org.apache.spark.util.Utils
-
Replaces all the {{APP_ID}} occurrences with the App Id.
- substituteAppNExecIds(String, String, String) - 类 中的静态方法org.apache.spark.util.Utils
-
Replaces all the {{EXECUTOR_ID}} occurrences with the Executor Id
and {{APP_ID}} occurrences with the App Id.
- substr(Column, Column) - 类 中的方法org.apache.spark.sql.Column
-
An expression that returns a substring.
- substr(int, int) - 类 中的方法org.apache.spark.sql.Column
-
An expression that returns a substring.
- substring(Column, int, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Substring starts at pos and is of length len when str is String type or
returns the slice of byte array that starts at pos in byte and is of length len
when str is Binary type
- substring_index(Column, String, int) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns the substring from string str before count occurrences of the delimiter delim.
- subtract(JavaDoubleRDD) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Return an RDD with the elements from this that are not in other.
- subtract(JavaDoubleRDD, int) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Return an RDD with the elements from this that are not in other.
- subtract(JavaDoubleRDD, Partitioner) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Return an RDD with the elements from this that are not in other.
- subtract(JavaPairRDD<K, V>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return an RDD with the elements from this that are not in other.
- subtract(JavaPairRDD<K, V>, int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return an RDD with the elements from this that are not in other.
- subtract(JavaPairRDD<K, V>, Partitioner) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return an RDD with the elements from this that are not in other.
- subtract(JavaRDD<T>) - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Return an RDD with the elements from this that are not in other.
- subtract(JavaRDD<T>, int) - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Return an RDD with the elements from this that are not in other.
- subtract(JavaRDD<T>, Partitioner) - 类 中的方法org.apache.spark.api.java.JavaRDD
-
Return an RDD with the elements from this that are not in other.
- subtract(Term) - 类 中的静态方法org.apache.spark.ml.feature.Dot
-
- subtract(Term) - 类 中的静态方法org.apache.spark.ml.feature.EmptyTerm
-
- subtract(Term) - 接口 中的方法org.apache.spark.ml.feature.Term
-
Fold by adding deletion terms to the left.
- subtract(BlockMatrix) - 类 中的方法org.apache.spark.mllib.linalg.distributed.BlockMatrix
-
Subtracts the given block matrix other from this block matrix: this - other.
- subtract(RDD<T>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return an RDD with the elements from this that are not in other.
- subtract(RDD<T>, int) - 类 中的方法org.apache.spark.rdd.RDD
-
Return an RDD with the elements from this that are not in other.
- subtract(RDD<T>, Partitioner, Ordering<T>) - 类 中的方法org.apache.spark.rdd.RDD
-
Return an RDD with the elements from this that are not in other.
- subtract(long, long) - 类 中的静态方法org.apache.spark.streaming.util.RawTextHelper
-
- subtractByKey(JavaPairRDD<K, W>) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return an RDD with the pairs from this whose keys are not in other.
- subtractByKey(JavaPairRDD<K, W>, int) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return an RDD with the pairs from this whose keys are not in other.
- subtractByKey(JavaPairRDD<K, W>, Partitioner) - 类 中的方法org.apache.spark.api.java.JavaPairRDD
-
Return an RDD with the pairs from this whose keys are not in other.
- subtractByKey(RDD<Tuple2<K, W>>, ClassTag<W>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Return an RDD with the pairs from this whose keys are not in other.
- subtractByKey(RDD<Tuple2<K, W>>, int, ClassTag<W>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Return an RDD with the pairs from this whose keys are not in other.
- subtractByKey(RDD<Tuple2<K, W>>, Partitioner, ClassTag<W>) - 类 中的方法org.apache.spark.rdd.PairRDDFunctions
-
Return an RDD with the pairs from this whose keys are not in other.
- subtractMetrics(TaskMetrics, TaskMetrics) - 类 中的静态方法org.apache.spark.status.LiveEntityHelpers
-
Subtract m2 values from m1.
- succeededTasks() - 类 中的方法org.apache.spark.status.api.v1.ExecutorStageSummary
-
- succeededTasks() - 类 中的方法org.apache.spark.status.LiveExecutorStageSummary
-
- Success() - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- success(T) - 类 中的静态方法org.apache.spark.ml.feature.RFormulaParser
-
- Success - org.apache.spark中的类
-
:: DeveloperApi ::
Task succeeded.
- Success() - 类 的构造器org.apache.spark.Success
-
- successful() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
- sum() - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Add up the elements in this RDD.
- Sum() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.EnsembleCombiningStrategy
-
- sum() - 类 中的方法org.apache.spark.rdd.DoubleRDDFunctions
-
Add up the elements in this RDD.
- sum(MapFunction<T, Double>) - 类 中的静态方法org.apache.spark.sql.expressions.javalang.typed
-
已过时。
Sum aggregate function for floating point (double) type.
- sum(Function1<IN, Object>) - 类 中的静态方法org.apache.spark.sql.expressions.scalalang.typed
-
已过时。
Sum aggregate function for floating point (double) type.
- sum(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the sum of all values in the expression.
- sum(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the sum of all values in the given column.
- sum(String...) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Compute the sum for each numeric columns for each group.
- sum(Seq<String>) - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
Compute the sum for each numeric columns for each group.
- sum() - 类 中的方法org.apache.spark.util.DoubleAccumulator
-
Returns the sum of elements added to the accumulator.
- sum() - 类 中的方法org.apache.spark.util.LongAccumulator
-
Returns the sum of elements added to the accumulator.
- sum() - 类 中的方法org.apache.spark.util.StatCounter
-
- sumApprox(long, Double) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Approximate operation to return the sum within a timeout.
- sumApprox(long) - 类 中的方法org.apache.spark.api.java.JavaDoubleRDD
-
Approximate operation to return the sum within a timeout.
- sumApprox(long, double) - 类 中的方法org.apache.spark.rdd.DoubleRDDFunctions
-
Approximate operation to return the sum within a timeout.
- sumDistinct(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the sum of distinct values in the expression.
- sumDistinct(String) - 类 中的静态方法org.apache.spark.sql.functions
-
Aggregate function: returns the sum of distinct values in the expression.
- sumLong(MapFunction<T, Long>) - 类 中的静态方法org.apache.spark.sql.expressions.javalang.typed
-
已过时。
Sum aggregate function for integral (long, i.e. 64 bit integer) type.
- sumLong(Function1<IN, Object>) - 类 中的静态方法org.apache.spark.sql.expressions.scalalang.typed
-
已过时。
Sum aggregate function for integral (long, i.e. 64 bit integer) type.
- Summarizer - org.apache.spark.ml.stat中的类
-
Tools for vectorized statistics on MLlib Vectors.
- Summarizer() - 类 的构造器org.apache.spark.ml.stat.Summarizer
-
- summary() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
Gets summary of model on training set.
- summary() - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
Gets summary of model on training set.
- summary() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
Gets summary of model on training set.
- summary() - 类 中的方法org.apache.spark.ml.clustering.KMeansModel
-
Gets summary of model on training set.
- summary() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
Gets R-like summary of model on training set.
- summary() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionModel
-
Gets summary (e.g. residuals, mse, r-squared ) of model on training set.
- summary(Column, Column) - 类 中的方法org.apache.spark.ml.stat.SummaryBuilder
-
Returns an aggregate object that contains the summary of the column with the requested metrics.
- summary(Column) - 类 中的方法org.apache.spark.ml.stat.SummaryBuilder
-
- summary() - 接口 中的方法org.apache.spark.ml.util.HasTrainingSummary
-
Gets summary of model on training set.
- summary(String...) - 类 中的方法org.apache.spark.sql.Dataset
-
Computes specified statistics for numeric and string columns.
- summary(Seq<String>) - 类 中的方法org.apache.spark.sql.Dataset
-
Computes specified statistics for numeric and string columns.
- SummaryBuilder - org.apache.spark.ml.stat中的类
-
A builder object that provides summary statistics about a given column.
- SummaryBuilder() - 类 的构造器org.apache.spark.ml.stat.SummaryBuilder
-
- supportColumnarReads(InputPartition) - 接口 中的方法org.apache.spark.sql.connector.read.PartitionReaderFactory
-
Returns true if the given
InputPartition should be read by Spark in a columnar way.
- supportDataType(DataType) - 类 中的方法org.apache.spark.sql.hive.orc.OrcFileFormat
-
- supportedFeatureSubsetStrategies() - 类 中的静态方法org.apache.spark.ml.classification.RandomForestClassifier
-
Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2
- supportedFeatureSubsetStrategies() - 类 中的静态方法org.apache.spark.ml.regression.RandomForestRegressor
-
Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2
- supportedFeatureSubsetStrategies() - 类 中的静态方法org.apache.spark.mllib.tree.RandomForest
-
List of supported feature subset sampling strategies.
- supportedImpurities() - 类 中的静态方法org.apache.spark.ml.classification.DecisionTreeClassifier
-
Accessor for supported impurities: entropy, gini
- supportedImpurities() - 类 中的静态方法org.apache.spark.ml.classification.RandomForestClassifier
-
Accessor for supported impurity settings: entropy, gini
- supportedImpurities() - 类 中的静态方法org.apache.spark.ml.regression.DecisionTreeRegressor
-
Accessor for supported impurities: variance
- supportedImpurities() - 类 中的静态方法org.apache.spark.ml.regression.RandomForestRegressor
-
Accessor for supported impurity settings: variance
- supportedLossTypes() - 类 中的静态方法org.apache.spark.ml.classification.GBTClassifier
-
Accessor for supported loss settings: logistic
- supportedLossTypes() - 类 中的静态方法org.apache.spark.ml.regression.GBTRegressor
-
Accessor for supported loss settings: squared (L2), absolute (L1)
- supportedOptimizers() - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- supportedOptimizers() - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
- supportedOptimizers() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
Supported values for Param optimizer.
- supportedSelectorTypes() - 类 中的静态方法org.apache.spark.mllib.feature.ChiSqSelector
-
Set of selector types that ChiSqSelector supports.
- SupportsDelete - org.apache.spark.sql.connector.catalog中的接口
-
A mix-in interface for
Table delete support.
- SupportsDynamicOverwrite - org.apache.spark.sql.connector.write中的接口
-
Write builder trait for tables that support dynamic partition overwrite.
- SupportsNamespaces - org.apache.spark.sql.connector.catalog中的接口
-
Catalog methods for working with namespaces.
- SupportsOverwrite - org.apache.spark.sql.connector.write中的接口
-
Write builder trait for tables that support overwrite by filter.
- SupportsPushDownFilters - org.apache.spark.sql.connector.read中的接口
-
- SupportsPushDownRequiredColumns - org.apache.spark.sql.connector.read中的接口
-
- SupportsRead - org.apache.spark.sql.connector.catalog中的接口
-
A mix-in interface of
Table, to indicate that it's readable.
- SupportsReportPartitioning - org.apache.spark.sql.connector.read中的接口
-
A mix in interface for
Scan.
- SupportsReportStatistics - org.apache.spark.sql.connector.read中的接口
-
A mix in interface for
Scan.
- SupportsTruncate - org.apache.spark.sql.connector.write中的接口
-
Write builder trait for tables that support truncation.
- SupportsWrite - org.apache.spark.sql.connector.catalog中的接口
-
A mix-in interface of
Table, to indicate that it's writable.
- surrogateDF() - 类 中的方法org.apache.spark.ml.feature.ImputerModel
-
- SVDPlusPlus - org.apache.spark.graphx.lib中的类
-
Implementation of SVD++ algorithm.
- SVDPlusPlus() - 类 的构造器org.apache.spark.graphx.lib.SVDPlusPlus
-
- SVDPlusPlus.Conf - org.apache.spark.graphx.lib中的类
-
Configuration parameters for SVDPlusPlus.
- SVMDataGenerator - org.apache.spark.mllib.util中的类
-
:: DeveloperApi ::
Generate sample data used for SVM.
- SVMDataGenerator() - 类 的构造器org.apache.spark.mllib.util.SVMDataGenerator
-
- SVMModel - org.apache.spark.mllib.classification中的类
-
Model for Support Vector Machines (SVMs).
- SVMModel(Vector, double) - 类 的构造器org.apache.spark.mllib.classification.SVMModel
-
- SVMWithSGD - org.apache.spark.mllib.classification中的类
-
Train a Support Vector Machine (SVM) using Stochastic Gradient Descent.
- SVMWithSGD() - 类 的构造器org.apache.spark.mllib.classification.SVMWithSGD
-
Construct a SVM object with default parameters: {stepSize: 1.0, numIterations: 100,
regParm: 0.01, miniBatchFraction: 1.0}.
- symbolToColumn(Symbol) - 类 中的方法org.apache.spark.sql.SQLImplicits
-
An implicit conversion that turns a Scala
Symbol into a
Column.
- symlink(File, File) - 类 中的静态方法org.apache.spark.util.Utils
-
Creates a symlink.
- symmetricEigs(Function1<DenseVector<Object>, DenseVector<Object>>, int, int, double, int) - 类 中的静态方法org.apache.spark.mllib.linalg.EigenValueDecomposition
-
Compute the leading k eigenvalues and eigenvectors on a symmetric square matrix using ARPACK.
- syr(double, Vector, DenseMatrix) - 类 中的静态方法org.apache.spark.ml.linalg.BLAS
-
A := alpha * x * x^T^ + A
- syr(double, Vector, DenseMatrix) - 类 中的静态方法org.apache.spark.mllib.linalg.BLAS
-
A := alpha * x * x^T^ + A
- SYSTEM_DEFAULT() - 类 中的静态方法org.apache.spark.sql.types.DecimalType
-
- systemProperties() - 类 中的方法org.apache.spark.status.api.v1.ApplicationEnvironmentInfo
-
- t() - 类 中的方法org.apache.spark.SerializableWritable
-
- Table - org.apache.spark.sql.catalog中的类
-
A table in Spark, as returned by the
listTables method in
Catalog.
- Table(String, String, String, String, boolean) - 类 的构造器org.apache.spark.sql.catalog.Table
-
- Table - org.apache.spark.sql.connector.catalog中的接口
-
An interface representing a logical structured data set of a data source.
- table(String) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Returns the specified table as a DataFrame.
- table() - 类 中的方法org.apache.spark.sql.hive.execution.InsertIntoHiveTable
-
- table(String) - 类 中的方法org.apache.spark.sql.SparkSession
-
Returns the specified table/view as a DataFrame.
- table(String) - 类 中的方法org.apache.spark.sql.SQLContext
-
Returns the specified table as a DataFrame.
- table(int) - 接口 中的方法org.apache.spark.ui.PagedTable
-
- TABLE_CLASS_NOT_STRIPED() - 类 中的静态方法org.apache.spark.ui.UIUtils
-
- TABLE_CLASS_STRIPED() - 类 中的静态方法org.apache.spark.ui.UIUtils
-
- TABLE_CLASS_STRIPED_SORTABLE() - 类 中的静态方法org.apache.spark.ui.UIUtils
-
- TableCapability - org.apache.spark.sql.connector.catalog中的枚举
-
Capabilities that can be provided by a
Table implementation.
- TableCatalog - org.apache.spark.sql.connector.catalog中的接口
-
Catalog methods for working with Tables.
- TableChange - org.apache.spark.sql.connector.catalog中的接口
-
TableChange subclasses represent requested changes to a table.
- TableChange.AddColumn - org.apache.spark.sql.connector.catalog中的类
-
A TableChange to add a field.
- TableChange.ColumnChange - org.apache.spark.sql.connector.catalog中的接口
-
- TableChange.DeleteColumn - org.apache.spark.sql.connector.catalog中的类
-
A TableChange to delete a field.
- TableChange.RemoveProperty - org.apache.spark.sql.connector.catalog中的类
-
A TableChange to remove a table property.
- TableChange.RenameColumn - org.apache.spark.sql.connector.catalog中的类
-
A TableChange to rename a field.
- TableChange.SetProperty - org.apache.spark.sql.connector.catalog中的类
-
A TableChange to set a table property.
- TableChange.UpdateColumnComment - org.apache.spark.sql.connector.catalog中的类
-
A TableChange to update the comment of a field.
- TableChange.UpdateColumnType - org.apache.spark.sql.connector.catalog中的类
-
A TableChange to update the type of a field.
- tableCssClass() - 接口 中的方法org.apache.spark.ui.PagedTable
-
- tableDesc() - 接口 中的方法org.apache.spark.sql.hive.execution.CreateHiveTableAsSelectBase
-
- tableDesc() - 类 中的方法org.apache.spark.sql.hive.execution.CreateHiveTableAsSelectCommand
-
- tableDesc() - 类 中的方法org.apache.spark.sql.hive.execution.OptimizedCreateHiveTableAsSelectCommand
-
- tableExists(String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Check if the table or view with the specified name exists.
- tableExists(String, String) - 类 中的方法org.apache.spark.sql.catalog.Catalog
-
Check if the table or view with the specified name exists in the specified database.
- tableExists(Identifier) - 类 中的方法org.apache.spark.sql.connector.catalog.DelegatingCatalogExtension
-
- tableExists(Identifier) - 接口 中的方法org.apache.spark.sql.connector.catalog.TableCatalog
-
Test whether a table exists using an
identifier from the catalog.
- tableExists(String, String) - 接口 中的方法org.apache.spark.sql.hive.client.HiveClient
-
Return whether a table/view with the specified name exists.
- tableId() - 接口 中的方法org.apache.spark.ui.PagedTable
-
- tableIdentifier() - 接口 中的方法org.apache.spark.sql.hive.execution.CreateHiveTableAsSelectBase
-
- tableNames() - 类 中的方法org.apache.spark.sql.SQLContext
-
Returns the names of tables in the current database as an array.
- tableNames(String) - 类 中的方法org.apache.spark.sql.SQLContext
-
Returns the names of tables in the given database as an array.
- tableProperty(String, String) - 接口 中的方法org.apache.spark.sql.CreateTableWriter
-
Add a table property.
- tableProperty(String, String) - 类 中的方法org.apache.spark.sql.DataFrameWriterV2
-
- TableProvider - org.apache.spark.sql.connector.catalog中的接口
-
The base interface for v2 data sources which don't have a real catalog.
- TableReader - org.apache.spark.sql.hive中的接口
-
A trait for subclasses that handle table scans.
- tables() - 类 中的方法org.apache.spark.sql.SQLContext
-
Returns a DataFrame containing names of existing tables in the current database.
- tables(String) - 类 中的方法org.apache.spark.sql.SQLContext
-
Returns a DataFrame containing names of existing tables in the given database.
- TableScan - org.apache.spark.sql.sources中的接口
-
A BaseRelation that can produce all of its tuples as an RDD of Row objects.
- tableType() - 类 中的方法org.apache.spark.sql.catalog.Table
-
- take(int) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Take the first num elements of the RDD.
- take(int) - 类 中的方法org.apache.spark.rdd.RDD
-
Take the first num elements of the RDD.
- take(int) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns the first n rows in the Dataset.
- takeAsList(int) - 类 中的方法org.apache.spark.sql.Dataset
-
Returns the first n rows in the Dataset as a list.
- takeAsync(int) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
The asynchronous version of the take action, which returns a
future for retrieving the first num elements of this RDD.
- takeAsync(int) - 类 中的方法org.apache.spark.rdd.AsyncRDDActions
-
Returns a future for retrieving the first num elements of the RDD.
- takeOrdered(int, Comparator<T>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Returns the first k (smallest) elements from this RDD as defined by
the specified Comparator[T] and maintains the order.
- takeOrdered(int) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Returns the first k (smallest) elements from this RDD using the
natural ordering for T while maintain the order.
- takeOrdered(int, Ordering<T>) - 类 中的方法org.apache.spark.rdd.RDD
-
Returns the first k (smallest) elements from this RDD as defined by the specified
implicit Ordering[T] and maintains the ordering.
- takeSample(boolean, int) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
- takeSample(boolean, int, long) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
- takeSample(boolean, int, long) - 类 中的方法org.apache.spark.rdd.RDD
-
Return a fixed-size sampled subset of this RDD in an array
- tallSkinnyQR(boolean) - 类 中的方法org.apache.spark.mllib.linalg.distributed.RowMatrix
-
- tan(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
- tan(String) - 类 中的静态方法org.apache.spark.sql.functions
-
- tanh(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
- tanh(String) - 类 中的静态方法org.apache.spark.sql.functions
-
- targetStorageLevel() - 类 中的方法org.apache.spark.graphx.impl.EdgeRDDImpl
-
- targetStorageLevel() - 类 中的方法org.apache.spark.graphx.impl.VertexRDDImpl
-
- task() - 类 中的方法org.apache.spark.CleanupTaskWeakReference
-
- TASK_DESERIALIZATION_TIME() - 类 中的静态方法org.apache.spark.ui.jobs.TaskDetailsClassNames
-
- TASK_DESERIALIZATION_TIME() - 类 中的静态方法org.apache.spark.ui.ToolTips
-
- TASK_INDEX() - 类 中的静态方法org.apache.spark.status.TaskIndexNames
-
- TASK_TIME() - 类 中的静态方法org.apache.spark.ui.ToolTips
-
- taskAttemptId() - 类 中的方法org.apache.spark.BarrierTaskContext
-
- taskAttemptId() - 类 中的方法org.apache.spark.TaskContext
-
An ID that is unique to this task attempt (within the same SparkContext, no two task attempts
will share the same attempt ID).
- TaskCommitDenied - org.apache.spark中的类
-
:: DeveloperApi ::
Task requested the driver to commit, but was denied.
- TaskCommitDenied(int, int, int) - 类 的构造器org.apache.spark.TaskCommitDenied
-
- TaskCommitMessage(Object) - 类 的构造器org.apache.spark.internal.io.FileCommitProtocol.TaskCommitMessage
-
- TaskCompletionListener - org.apache.spark.util中的接口
-
:: DeveloperApi ::
Listener providing a callback function to invoke when a task's execution completes.
- TaskContext - org.apache.spark中的类
-
Contextual information about a task which can be read or mutated during
execution.
- TaskContext() - 类 的构造器org.apache.spark.TaskContext
-
- TaskData - org.apache.spark.status.api.v1中的类
-
- TaskDetailsClassNames - org.apache.spark.ui.jobs中的类
-
Names of the CSS classes corresponding to each type of task detail.
- TaskDetailsClassNames() - 类 的构造器org.apache.spark.ui.jobs.TaskDetailsClassNames
-
- taskEndFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- TaskEndReason - org.apache.spark中的接口
-
:: DeveloperApi ::
Various possible reasons why a task ended.
- taskEndReasonFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- taskEndReasonToJson(TaskEndReason) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- taskEndToJson(SparkListenerTaskEnd) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- taskExecutorMetrics() - 类 中的方法org.apache.spark.scheduler.SparkListenerTaskEnd
-
- TaskFailedReason - org.apache.spark中的接口
-
:: DeveloperApi ::
Various possible reasons why a task failed.
- TaskFailureListener - org.apache.spark.util中的接口
-
:: DeveloperApi ::
Listener providing a callback function to invoke when a task's execution encounters an error.
- taskFailures() - 类 中的方法org.apache.spark.scheduler.SparkListenerExecutorBlacklisted
-
- taskFailures() - 类 中的方法org.apache.spark.scheduler.SparkListenerExecutorBlacklistedForStage
-
- taskGettingResultFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- taskGettingResultToJson(SparkListenerTaskGettingResult) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- taskId() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.KillTask
-
- taskId() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.StatusUpdate
-
- taskId() - 类 中的方法org.apache.spark.scheduler.local.KillTask
-
- taskId() - 类 中的方法org.apache.spark.scheduler.local.StatusUpdate
-
- taskId() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
- taskId() - 类 中的方法org.apache.spark.status.api.v1.TaskData
-
- taskId() - 类 中的方法org.apache.spark.storage.TaskResultBlockId
-
- TaskIndexNames - org.apache.spark.status中的类
-
Tasks have a lot of indices that are used in a few different places.
- TaskIndexNames() - 类 的构造器org.apache.spark.status.TaskIndexNames
-
- taskInfo() - 类 中的方法org.apache.spark.scheduler.SparkListenerTaskEnd
-
- taskInfo() - 类 中的方法org.apache.spark.scheduler.SparkListenerTaskGettingResult
-
- taskInfo() - 类 中的方法org.apache.spark.scheduler.SparkListenerTaskStart
-
- TaskInfo - org.apache.spark.scheduler中的类
-
:: DeveloperApi ::
Information about a running task attempt inside a TaskSet.
- TaskInfo(long, int, int, long, String, String, Enumeration.Value, boolean) - 类 的构造器org.apache.spark.scheduler.TaskInfo
-
- taskInfoFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- taskInfoToJson(TaskInfo) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- TaskKilled - org.apache.spark中的类
-
:: DeveloperApi ::
Task was killed intentionally and needs to be rescheduled.
- TaskKilled(String, Seq<AccumulableInfo>, Seq<AccumulatorV2<?, ?>>, Seq<Object>) - 类 的构造器org.apache.spark.TaskKilled
-
- TaskKilledException - org.apache.spark中的异常错误
-
:: DeveloperApi ::
Exception thrown when a task is explicitly killed (i.e., task failure is expected).
- TaskKilledException(String) - 异常错误 的构造器org.apache.spark.TaskKilledException
-
- TaskKilledException() - 异常错误 的构造器org.apache.spark.TaskKilledException
-
- taskLocality() - 类 中的方法org.apache.spark.scheduler.TaskInfo
-
- TaskLocality - org.apache.spark.scheduler中的类
-
- TaskLocality() - 类 的构造器org.apache.spark.scheduler.TaskLocality
-
- taskLocality() - 类 中的方法org.apache.spark.status.api.v1.TaskData
-
- TaskLocation - org.apache.spark.scheduler中的接口
-
A location where a task should run.
- TaskMetricDistributions - org.apache.spark.status.api.v1中的类
-
- taskMetrics() - 类 中的方法org.apache.spark.BarrierTaskContext
-
- taskMetrics() - 类 中的方法org.apache.spark.scheduler.SparkListenerTaskEnd
-
- taskMetrics() - 类 中的方法org.apache.spark.scheduler.StageInfo
-
- taskMetrics() - 类 中的方法org.apache.spark.status.api.v1.TaskData
-
- TaskMetrics - org.apache.spark.status.api.v1中的类
-
- taskMetrics() - 类 中的方法org.apache.spark.TaskContext
-
- taskMetricsFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- taskMetricsToJson(TaskMetrics) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- TaskResult<T> - org.apache.spark.scheduler中的接口
-
- TASKRESULT() - 类 中的静态方法org.apache.spark.storage.BlockId
-
- TaskResultBlockId - org.apache.spark.storage中的类
-
- TaskResultBlockId(long) - 类 的构造器org.apache.spark.storage.TaskResultBlockId
-
- TaskResultLost - org.apache.spark中的类
-
:: DeveloperApi ::
The task finished successfully, but the result was lost from the executor's block manager before
it was fetched.
- TaskResultLost() - 类 的构造器org.apache.spark.TaskResultLost
-
- tasks() - 类 中的方法org.apache.spark.status.api.v1.StageData
-
- TaskScheduler - org.apache.spark.scheduler中的接口
-
Low-level task scheduler interface, currently implemented exclusively by
TaskSchedulerImpl.
- TaskSchedulerIsSet - org.apache.spark中的类
-
An event that SparkContext uses to notify HeartbeatReceiver that SparkContext.taskScheduler is
created.
- TaskSchedulerIsSet() - 类 的构造器org.apache.spark.TaskSchedulerIsSet
-
- TaskSorting - org.apache.spark.status.api.v1中的枚举
-
- taskStartFromJson(JsonAST.JValue) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- taskStartToJson(SparkListenerTaskStart) - 类 中的静态方法org.apache.spark.util.JsonProtocol
-
- TaskState - org.apache.spark中的类
-
- TaskState() - 类 的构造器org.apache.spark.TaskState
-
- taskSucceeded(int, Object) - 接口 中的方法org.apache.spark.scheduler.JobListener
-
- taskTime() - 类 中的方法org.apache.spark.status.api.v1.ExecutorStageSummary
-
- taskTime() - 类 中的方法org.apache.spark.status.LiveExecutorStageSummary
-
- taskType() - 类 中的方法org.apache.spark.scheduler.SparkListenerTaskEnd
-
- TEMP_DIR_SHUTDOWN_PRIORITY() - 类 中的静态方法org.apache.spark.util.ShutdownHookManager
-
The shutdown priority of temp directory must be lower than the SparkContext shutdown
priority.
- TEMP_LOCAL() - 类 中的静态方法org.apache.spark.storage.BlockId
-
- TEMP_SHUFFLE() - 类 中的静态方法org.apache.spark.storage.BlockId
-
- tempFileWith(File) - 类 中的静态方法org.apache.spark.util.Utils
-
Returns a path of temporary file which is in the same directory with path.
- TeradataDialect - org.apache.spark.sql.jdbc中的类
-
- TeradataDialect() - 类 的构造器org.apache.spark.sql.jdbc.TeradataDialect
-
- Term - org.apache.spark.ml.feature中的接口
-
R formula terms.
- terminateProcess(Process, long) - 类 中的静态方法org.apache.spark.util.Utils
-
Terminates a process waiting for at most the specified duration.
- test(Dataset<Row>, String, String) - 类 中的静态方法org.apache.spark.ml.stat.ChiSquareTest
-
Conduct Pearson's independence test for every feature against the label.
- test(Dataset<?>, String, String, double...) - 类 中的静态方法org.apache.spark.ml.stat.KolmogorovSmirnovTest
-
Convenience function to conduct a one-sample, two-sided Kolmogorov-Smirnov test for probability
distribution equality.
- test(Dataset<?>, String, Function1<Object, Object>) - 类 中的静态方法org.apache.spark.ml.stat.KolmogorovSmirnovTest
-
- test(Dataset<?>, String, Function<Double, Double>) - 类 中的静态方法org.apache.spark.ml.stat.KolmogorovSmirnovTest
-
- test(Dataset<?>, String, String, Seq<Object>) - 类 中的静态方法org.apache.spark.ml.stat.KolmogorovSmirnovTest
-
- TEST() - 类 中的静态方法org.apache.spark.storage.BlockId
-
- TEST_ACCUM() - 类 中的静态方法org.apache.spark.InternalAccumulator
-
- TEST_MEMORY() - 类 中的静态方法org.apache.spark.internal.config.Tests
-
- TEST_N_CORES_EXECUTOR() - 类 中的静态方法org.apache.spark.internal.config.Tests
-
- TEST_N_EXECUTORS_HOST() - 类 中的静态方法org.apache.spark.internal.config.Tests
-
- TEST_N_HOSTS() - 类 中的静态方法org.apache.spark.internal.config.Tests
-
- TEST_NO_STAGE_RETRY() - 类 中的静态方法org.apache.spark.internal.config.Tests
-
- TEST_RESERVED_MEMORY() - 类 中的静态方法org.apache.spark.internal.config.Tests
-
- TEST_SCHEDULE_INTERVAL() - 类 中的静态方法org.apache.spark.internal.config.Tests
-
- TEST_USE_COMPRESSED_OOPS_KEY() - 类 中的静态方法org.apache.spark.internal.config.Tests
-
- testCommandAvailable(String) - 类 中的静态方法org.apache.spark.TestUtils
-
Test if a command is available.
- testOneSample(RDD<Object>, String, double...) - 类 中的静态方法org.apache.spark.mllib.stat.test.KolmogorovSmirnovTest
-
A convenience function that allows running the KS test for 1 set of sample data against
a named distribution
- testOneSample(RDD<Object>, Function1<Object, Object>) - 类 中的静态方法org.apache.spark.mllib.stat.test.KolmogorovSmirnovTest
-
- testOneSample(RDD<Object>, RealDistribution) - 类 中的静态方法org.apache.spark.mllib.stat.test.KolmogorovSmirnovTest
-
- testOneSample(RDD<Object>, String, Seq<Object>) - 类 中的静态方法org.apache.spark.mllib.stat.test.KolmogorovSmirnovTest
-
- TestResult<DF> - org.apache.spark.mllib.stat.test中的接口
-
Trait for hypothesis test results.
- Tests - org.apache.spark.internal.config中的类
-
- Tests() - 类 的构造器org.apache.spark.internal.config.Tests
-
- TestUtils - org.apache.spark中的类
-
Utilities for tests.
- TestUtils() - 类 的构造器org.apache.spark.TestUtils
-
- text(String...) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads text files and returns a DataFrame whose schema starts with a string column named
"value", and followed by partitioned columns if there are any.
- text(String) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads text files and returns a DataFrame whose schema starts with a string column named
"value", and followed by partitioned columns if there are any.
- text(Seq<String>) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads text files and returns a DataFrame whose schema starts with a string column named
"value", and followed by partitioned columns if there are any.
- text(String) - 类 中的方法org.apache.spark.sql.DataFrameWriter
-
Saves the content of the DataFrame in a text file at the specified path.
- text(String) - 类 中的方法org.apache.spark.sql.streaming.DataStreamReader
-
Loads text files and returns a DataFrame whose schema starts with a string column named
"value", and followed by partitioned columns if there are any.
- textFile(String) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Read a text file from HDFS, a local file system (available on all nodes), or any
Hadoop-supported file system URI, and return it as an RDD of Strings.
- textFile(String, int) - 类 中的方法org.apache.spark.api.java.JavaSparkContext
-
Read a text file from HDFS, a local file system (available on all nodes), or any
Hadoop-supported file system URI, and return it as an RDD of Strings.
- textFile(String, int) - 类 中的方法org.apache.spark.SparkContext
-
Read a text file from HDFS, a local file system (available on all nodes), or any
Hadoop-supported file system URI, and return it as an RDD of Strings.
- textFile(String...) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads text files and returns a
Dataset of String.
- textFile(String) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads text files and returns a
Dataset of String.
- textFile(Seq<String>) - 类 中的方法org.apache.spark.sql.DataFrameReader
-
Loads text files and returns a
Dataset of String.
- textFile(String) - 类 中的方法org.apache.spark.sql.streaming.DataStreamReader
-
Loads text file(s) and returns a Dataset of String.
- textFileStream(String) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Create an input stream that monitors a Hadoop-compatible filesystem
for new files and reads them as text files (using key as LongWritable, value
as Text and input format as TextInputFormat).
- textFileStream(String) - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Create an input stream that monitors a Hadoop-compatible filesystem
for new files and reads them as text files (using key as LongWritable, value
as Text and input format as TextInputFormat).
- textResponderToServlet(Function1<HttpServletRequest, String>) - 类 中的静态方法org.apache.spark.ui.JettyUtils
-
- thenComparing(Comparator<? super T>) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- thenComparing(Function<? super T, ? extends U>, Comparator<? super U>) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- thenComparing(Function<? super T, ? extends U>) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- thenComparing(Comparator<? super T>) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- thenComparing(Function<? super T, ? extends U>, Comparator<? super U>) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- thenComparing(Function<? super T, ? extends U>) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- thenComparing(Comparator<? super T>) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- thenComparing(Function<? super T, ? extends U>, Comparator<? super U>) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- thenComparing(Function<? super T, ? extends U>) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- thenComparing(Comparator<? super T>) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- thenComparing(Function<? super T, ? extends U>, Comparator<? super U>) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- thenComparing(Function<? super T, ? extends U>) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- thenComparing(Comparator<? super T>) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- thenComparing(Function<? super T, ? extends U>, Comparator<? super U>) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- thenComparing(Function<? super T, ? extends U>) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- thenComparing(Comparator<? super T>) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- thenComparing(Function<? super T, ? extends U>, Comparator<? super U>) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- thenComparing(Function<? super T, ? extends U>) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- thenComparing(Comparator<? super T>) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- thenComparing(Function<? super T, ? extends U>, Comparator<? super U>) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- thenComparing(Function<? super T, ? extends U>) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- thenComparingDouble(ToDoubleFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- thenComparingDouble(ToDoubleFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- thenComparingDouble(ToDoubleFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- thenComparingDouble(ToDoubleFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- thenComparingDouble(ToDoubleFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- thenComparingDouble(ToDoubleFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- thenComparingDouble(ToDoubleFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- thenComparingInt(ToIntFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- thenComparingInt(ToIntFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- thenComparingInt(ToIntFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- thenComparingInt(ToIntFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- thenComparingInt(ToIntFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- thenComparingInt(ToIntFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- thenComparingInt(ToIntFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- thenComparingLong(ToLongFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- thenComparingLong(ToLongFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- thenComparingLong(ToLongFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- thenComparingLong(ToLongFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- thenComparingLong(ToLongFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- thenComparingLong(ToLongFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- thenComparingLong(ToLongFunction<? super T>) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- theta() - 类 中的方法org.apache.spark.ml.classification.NaiveBayesModel
-
- theta() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV1_0$.Data
-
- theta() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV2_0$.Data
-
- theta() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel
-
- thisClassName() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV1_0$
-
Hard-code class name string in case it changes in the future
- thisClassName() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV2_0$
-
Hard-code class name string in case it changes in the future
- thisClassName() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$
-
- thisFormatVersion() - 类 中的方法org.apache.spark.mllib.classification.impl.GLMClassificationModel.SaveLoadV1_0$
-
- thisFormatVersion() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV1_0$
-
- thisFormatVersion() - 类 中的方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV2_0$
-
- thisFormatVersion() - 类 中的方法org.apache.spark.mllib.regression.impl.GLMRegressionModel.SaveLoadV1_0$
-
- thisFormatVersion() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$
-
- threadCount() - 接口 中的方法org.apache.spark.rpc.IsolatedRpcEndpoint
-
How many threads to use for delivering messages.
- threadId() - 类 中的方法org.apache.spark.status.api.v1.ThreadStackTrace
-
- threadName() - 类 中的方法org.apache.spark.status.api.v1.ThreadStackTrace
-
- ThreadSafeRpcEndpoint - org.apache.spark.rpc中的接口
-
A trait that requires RpcEnv thread-safely sending messages to it.
- ThreadStackTrace - org.apache.spark.status.api.v1中的类
-
- ThreadStackTrace(long, String, Thread.State, StackTrace, Option<Object>, String, Seq<String>) - 类 的构造器org.apache.spark.status.api.v1.ThreadStackTrace
-
- threadState() - 类 中的方法org.apache.spark.status.api.v1.ThreadStackTrace
-
- ThreadUtils - org.apache.spark.util中的类
-
- ThreadUtils() - 类 的构造器org.apache.spark.util.ThreadUtils
-
- threshold() - 类 中的方法org.apache.spark.ml.classification.LinearSVC
-
- threshold() - 类 中的方法org.apache.spark.ml.classification.LinearSVCModel
-
- threshold() - 接口 中的方法org.apache.spark.ml.classification.LinearSVCParams
-
Param for threshold in binary classification prediction.
- threshold() - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
- threshold() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- threshold() - 类 中的方法org.apache.spark.ml.feature.Binarizer
-
Param for threshold used to binarize continuous features.
- threshold() - 接口 中的方法org.apache.spark.ml.param.shared.HasThreshold
-
Param for threshold in binary classification prediction, in range [0, 1].
- threshold() - 类 中的方法org.apache.spark.ml.tree.ContinuousSplit
-
- threshold() - 类 中的方法org.apache.spark.mllib.classification.impl.GLMClassificationModel.SaveLoadV1_0$.Data
-
- threshold() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.SplitData
-
- threshold() - 类 中的方法org.apache.spark.mllib.tree.model.Split
-
- thresholds() - 类 中的方法org.apache.spark.ml.classification.ProbabilisticClassificationModel
-
- thresholds() - 类 中的方法org.apache.spark.ml.classification.ProbabilisticClassifier
-
- thresholds() - 类 中的方法org.apache.spark.ml.feature.Binarizer
-
Array of threshold used to binarize continuous features.
- thresholds() - 接口 中的方法org.apache.spark.ml.param.shared.HasThresholds
-
Param for Thresholds in multi-class classification to adjust the probability of predicting each class.
- thresholds() - 类 中的方法org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
-
Returns thresholds in descending order.
- throughOrigin() - 类 中的方法org.apache.spark.ml.evaluation.RegressionEvaluator
-
param for whether the regression is through the origin.
- throwBalls(int, RDD<?>, double, org.apache.spark.rdd.DefaultPartitionCoalescer.PartitionLocations) - 类 中的方法org.apache.spark.rdd.DefaultPartitionCoalescer
-
- time() - 类 中的方法org.apache.spark.scheduler.SparkListenerApplicationEnd
-
- time() - 类 中的方法org.apache.spark.scheduler.SparkListenerApplicationStart
-
- time() - 类 中的方法org.apache.spark.scheduler.SparkListenerBlockManagerAdded
-
- time() - 类 中的方法org.apache.spark.scheduler.SparkListenerBlockManagerRemoved
-
- time() - 类 中的方法org.apache.spark.scheduler.SparkListenerExecutorAdded
-
- time() - 类 中的方法org.apache.spark.scheduler.SparkListenerExecutorBlacklisted
-
- time() - 类 中的方法org.apache.spark.scheduler.SparkListenerExecutorBlacklistedForStage
-
- time() - 类 中的方法org.apache.spark.scheduler.SparkListenerExecutorRemoved
-
- time() - 类 中的方法org.apache.spark.scheduler.SparkListenerExecutorUnblacklisted
-
- time() - 类 中的方法org.apache.spark.scheduler.SparkListenerJobEnd
-
- time() - 类 中的方法org.apache.spark.scheduler.SparkListenerJobStart
-
- time() - 类 中的方法org.apache.spark.scheduler.SparkListenerNodeBlacklisted
-
- time() - 类 中的方法org.apache.spark.scheduler.SparkListenerNodeBlacklistedForStage
-
- time() - 类 中的方法org.apache.spark.scheduler.SparkListenerNodeUnblacklisted
-
- time(Function0<T>) - 类 中的方法org.apache.spark.sql.SparkSession
-
Executes some code block and prints to stdout the time taken to execute the block.
- time() - 异常错误 中的方法org.apache.spark.sql.streaming.StreamingQueryException
-
Time when the exception occurred
- time() - 类 中的方法org.apache.spark.streaming.scheduler.StreamingListenerStreamingStarted
-
- Time - org.apache.spark.streaming中的类
-
This is a simple class that represents an absolute instant of time.
- Time(long) - 类 的构造器org.apache.spark.streaming.Time
-
- timeFromString(String, TimeUnit) - 类 中的静态方法org.apache.spark.internal.config.ConfigHelpers
-
- timeIt(int, Function0<BoxedUnit>, Option<Function0<BoxedUnit>>) - 类 中的静态方法org.apache.spark.util.Utils
-
Timing method based on iterations that permit JVM JIT optimization.
- timeout(Duration) - 类 中的方法org.apache.spark.streaming.StateSpec
-
Set the duration after which the state of an idle key will be removed.
- TIMER() - 类 中的静态方法org.apache.spark.metrics.sink.StatsdMetricType
-
- times(byte, byte) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- times(Decimal, Decimal) - 接口 中的方法org.apache.spark.sql.types.Decimal.DecimalIsConflicted
-
- times(Decimal, Decimal) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- times(double, double) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- times(float, float) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- times(int, int) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- times(long, long) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- times(short, short) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- times(int) - 类 中的方法org.apache.spark.streaming.Duration
-
- times(int, Function0<BoxedUnit>) - 类 中的静态方法org.apache.spark.util.Utils
-
Method executed for repeating a task for side effects.
- timestamp() - 类 中的方法org.apache.spark.sql.ColumnName
-
Creates a new StructField of type timestamp.
- TIMESTAMP() - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for nullable timestamp type.
- timestamp() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryProgress
-
- TimestampType - 类 中的静态变量org.apache.spark.sql.types.DataTypes
-
Gets the TimestampType object.
- TimestampType - org.apache.spark.sql.types中的类
-
The timestamp type represents a time instant in microsecond precision.
- TimestampType() - 类 的构造器org.apache.spark.sql.types.TimestampType
-
- timeStringAsMs(String) - 类 中的静态方法org.apache.spark.util.Utils
-
Convert a time parameter such as (50s, 100ms, or 250us) to milliseconds for internal use.
- timeStringAsSeconds(String) - 类 中的静态方法org.apache.spark.util.Utils
-
Convert a time parameter such as (50s, 100ms, or 250us) to seconds for internal use.
- timeTakenMs(Function0<T>) - 类 中的静态方法org.apache.spark.util.Utils
-
Records the duration of running `body`.
- timeToString(long, TimeUnit) - 类 中的静态方法org.apache.spark.internal.config.ConfigHelpers
-
- TimeTrackingOutputStream - org.apache.spark.storage中的类
-
Intercepts write calls and tracks total time spent writing in order to update shuffle write
metrics.
- TimeTrackingOutputStream(ShuffleWriteMetricsReporter, OutputStream) - 类 的构造器org.apache.spark.storage.TimeTrackingOutputStream
-
- timeUnit() - 类 中的方法org.apache.spark.mllib.clustering.StreamingKMeans
-
- TIMING_DATA() - 类 中的静态方法org.apache.spark.api.r.SpecialLengths
-
- to(Time, Duration) - 类 中的方法org.apache.spark.streaming.Time
-
- to_csv(Column, Map<String, String>) - 类 中的静态方法org.apache.spark.sql.functions
-
(Java-specific) Converts a column containing a StructType into a CSV string with
the specified schema.
- to_csv(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Converts a column containing a StructType into a CSV string with the specified schema.
- to_date(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Converts the column into DateType by casting rules to DateType.
- to_date(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Converts the column into a DateType with a specified format
See DateTimeFormatter for valid date and time format patterns
- to_json(Column, Map<String, String>) - 类 中的静态方法org.apache.spark.sql.functions
-
(Scala-specific) Converts a column containing a StructType, ArrayType or
a MapType into a JSON string with the specified schema.
- to_json(Column, Map<String, String>) - 类 中的静态方法org.apache.spark.sql.functions
-
(Java-specific) Converts a column containing a StructType, ArrayType or
a MapType into a JSON string with the specified schema.
- to_json(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Converts a column containing a StructType, ArrayType or
a MapType into a JSON string with the specified schema.
- to_timestamp(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Converts to a timestamp by casting rules to TimestampType.
- to_timestamp(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Converts time string with the given pattern to timestamp.
- to_utc_timestamp(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
- to_utc_timestamp(Column, Column) - 类 中的静态方法org.apache.spark.sql.functions
-
- toApacheCommonsStats(StatCounter) - 接口 中的方法org.apache.spark.mllib.stat.test.StreamingTestMethod
-
Implicit adapter to convert between streaming summary statistics type and the type required by
the t-testing libraries.
- toApi() - 类 中的方法org.apache.spark.status.LiveRDDDistribution
-
- toApi() - 类 中的方法org.apache.spark.status.LiveStage
-
- toArray() - 类 中的方法org.apache.spark.input.PortableDataStream
-
Read the file as a byte array
- toArray() - 类 中的方法org.apache.spark.ml.linalg.DenseVector
-
- toArray() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Converts to a dense array in column major.
- toArray() - 类 中的方法org.apache.spark.ml.linalg.SparseVector
-
- toArray() - 接口 中的方法org.apache.spark.ml.linalg.Vector
-
Converts the instance to a double array.
- toArray() - 类 中的方法org.apache.spark.mllib.linalg.DenseVector
-
- toArray() - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
Converts to a dense array in column major.
- toArray() - 类 中的方法org.apache.spark.mllib.linalg.SparseVector
-
- toArray() - 接口 中的方法org.apache.spark.mllib.linalg.Vector
-
Converts the instance to a double array.
- toArrowField(String, DataType, boolean, String) - 类 中的静态方法org.apache.spark.sql.util.ArrowUtils
-
Maps field from Spark to Arrow.
- toArrowSchema(StructType, String) - 类 中的静态方法org.apache.spark.sql.util.ArrowUtils
-
Maps schema from Spark to Arrow.
- toArrowType(DataType, String) - 类 中的静态方法org.apache.spark.sql.util.ArrowUtils
-
Maps data type from Spark to Arrow.
- toBatch() - 接口 中的方法org.apache.spark.sql.connector.read.Scan
-
Returns the physical representation of this scan for batch query.
- toBigDecimal() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- toBlockMatrix() - 类 中的方法org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
-
Converts to BlockMatrix.
- toBlockMatrix(int, int) - 类 中的方法org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
-
Converts to BlockMatrix.
- toBlockMatrix() - 类 中的方法org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
-
Converts to BlockMatrix.
- toBlockMatrix(int, int) - 类 中的方法org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
-
Converts to BlockMatrix.
- toBoolean(String, String) - 类 中的静态方法org.apache.spark.internal.config.ConfigHelpers
-
- toBooleanArray() - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- toBreeze() - 接口 中的方法org.apache.spark.mllib.linalg.distributed.DistributedMatrix
-
Collects data and assembles a local dense breeze matrix (for test only).
- toByte() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- toByteArray() - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- toByteArray() - 类 中的方法org.apache.spark.util.sketch.CountMinSketch
-
- toByteBuffer() - 接口 中的方法org.apache.spark.storage.BlockData
-
- toByteBuffer() - 类 中的方法org.apache.spark.storage.DiskBlockData
-
- toCatalystDecimal(HiveDecimalObjectInspector, Object) - 类 中的静态方法org.apache.spark.sql.hive.HiveShim
-
- toChunkedByteBuffer(Function1<Object, ByteBuffer>) - 接口 中的方法org.apache.spark.storage.BlockData
-
- toChunkedByteBuffer(Function1<Object, ByteBuffer>) - 类 中的方法org.apache.spark.storage.DiskBlockData
-
- toColumn() - 类 中的方法org.apache.spark.sql.expressions.Aggregator
-
Returns this Aggregator as a TypedColumn that can be used in Dataset.
- toContinuousStream(String) - 接口 中的方法org.apache.spark.sql.connector.read.Scan
-
Returns the physical representation of this scan for streaming query with continuous mode.
- toCoordinateMatrix() - 类 中的方法org.apache.spark.mllib.linalg.distributed.BlockMatrix
-
Converts to CoordinateMatrix.
- toCoordinateMatrix() - 类 中的方法org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
-
- toCryptoConf(SparkConf) - 类 中的静态方法org.apache.spark.security.CryptoStreamUtils
-
- toDataFrame(JavaRDD<byte[]>, StructType, SparkSession) - 类 中的静态方法org.apache.spark.sql.api.r.SQLUtils
-
R callable function to create a DataFrame from a JavaRDD of serialized
ArrowRecordBatches.
- toDDL() - 类 中的方法org.apache.spark.sql.types.StructField
-
Returns a string containing a schema in DDL format.
- toDDL() - 类 中的方法org.apache.spark.sql.types.StructType
-
Returns a string containing a schema in DDL format.
- toDebugString() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
A description of this RDD and its recursive dependencies for debugging.
- toDebugString() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeModel
-
Full description of model
- toDebugString() - 接口 中的方法org.apache.spark.ml.tree.TreeEnsembleModel
-
Full description of model
- toDebugString() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel
-
Print the full model to a string.
- toDebugString() - 类 中的方法org.apache.spark.rdd.RDD
-
A description of this RDD and its recursive dependencies for debugging.
- toDebugString() - 类 中的方法org.apache.spark.SparkConf
-
Return a string listing all keys and values, one per line.
- toDebugString() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- toDense() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Converts this matrix to a dense matrix while maintaining the layout of the current matrix.
- toDense() - 接口 中的方法org.apache.spark.ml.linalg.Vector
-
Converts this vector to a dense vector.
- toDense() - 类 中的方法org.apache.spark.mllib.linalg.SparseMatrix
-
Generate a DenseMatrix from the given SparseMatrix.
- toDense() - 接口 中的方法org.apache.spark.mllib.linalg.Vector
-
Converts this vector to a dense vector.
- toDenseColMajor() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Converts this matrix to a dense matrix in column major order.
- toDenseMatrix(boolean) - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Converts this matrix to a dense matrix.
- toDenseRowMajor() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Converts this matrix to a dense matrix in row major order.
- toDF(String...) - 类 中的方法org.apache.spark.sql.Dataset
-
Converts this strongly typed collection of data to generic DataFrame with columns renamed.
- toDF() - 类 中的方法org.apache.spark.sql.Dataset
-
Converts this strongly typed collection of data to generic Dataframe.
- toDF(Seq<String>) - 类 中的方法org.apache.spark.sql.Dataset
-
Converts this strongly typed collection of data to generic DataFrame with columns renamed.
- toDF() - 类 中的方法org.apache.spark.sql.DatasetHolder
-
- toDF(Seq<String>) - 类 中的方法org.apache.spark.sql.DatasetHolder
-
- toDouble(byte) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- toDouble(Decimal) - 接口 中的方法org.apache.spark.sql.types.Decimal.DecimalIsConflicted
-
- toDouble() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- toDouble(Decimal) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- toDouble(double) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- toDouble(float) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- toDouble(int) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- toDouble(long) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- toDouble(short) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- toDoubleArray() - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- toDS() - 类 中的方法org.apache.spark.sql.DatasetHolder
-
- toEdgeTriplet() - 类 中的方法org.apache.spark.graphx.EdgeContext
-
Converts the edge and vertex properties into an
EdgeTriplet for convenience.
- toErrorString() - 类 中的方法org.apache.spark.ExceptionFailure
-
- toErrorString() - 类 中的方法org.apache.spark.ExecutorLostFailure
-
- toErrorString() - 类 中的方法org.apache.spark.FetchFailed
-
- toErrorString() - 类 中的静态方法org.apache.spark.Resubmitted
-
- toErrorString() - 类 中的方法org.apache.spark.TaskCommitDenied
-
- toErrorString() - 接口 中的方法org.apache.spark.TaskFailedReason
-
Error message displayed in the web UI.
- toErrorString() - 类 中的方法org.apache.spark.TaskKilled
-
- toErrorString() - 类 中的静态方法org.apache.spark.TaskResultLost
-
- toErrorString() - 类 中的静态方法org.apache.spark.UnknownReason
-
- toFloat(byte) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- toFloat(Decimal) - 接口 中的方法org.apache.spark.sql.types.Decimal.DecimalIsConflicted
-
- toFloat() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- toFloat(Decimal) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- toFloat(double) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- toFloat(float) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- toFloat(int) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- toFloat(long) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- toFloat(short) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- toFloatArray() - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- toFormattedString() - 类 中的方法org.apache.spark.streaming.Duration
-
- toIndexedRowMatrix() - 类 中的方法org.apache.spark.mllib.linalg.distributed.BlockMatrix
-
Converts to IndexedRowMatrix.
- toIndexedRowMatrix() - 类 中的方法org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
-
Converts to IndexedRowMatrix.
- toInputStream() - 接口 中的方法org.apache.spark.storage.BlockData
-
- toInputStream() - 类 中的方法org.apache.spark.storage.DiskBlockData
-
- toInspector(DataType) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
- toInspector(Expression) - 接口 中的方法org.apache.spark.sql.hive.HiveInspectors
-
Map the catalyst expression to ObjectInspector, however,
if the expression is Literal or foldable, a constant writable object inspector returns;
Otherwise, we always get the object inspector according to its data type(in catalyst)
- toInspector(DataType) - 类 中的静态方法org.apache.spark.sql.hive.orc.OrcFileFormat
-
- toInspector(Expression) - 类 中的静态方法org.apache.spark.sql.hive.orc.OrcFileFormat
-
- toInt(byte) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- toInt(Decimal) - 接口 中的方法org.apache.spark.sql.types.Decimal.DecimalIsConflicted
-
- toInt() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- toInt(Decimal) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- toInt(double) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- toInt(float) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- toInt(int) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- toInt(long) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- toInt(short) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- toInt() - 类 中的方法org.apache.spark.storage.StorageLevel
-
- toIntArray() - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- toJavaBigDecimal() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- toJavaBigInteger() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- toJavaDStream() - 类 中的方法org.apache.spark.streaming.api.java.JavaPairDStream
-
Convert to a JavaDStream
- toJavaRDD() - 类 中的方法org.apache.spark.rdd.RDD
-
- toJavaRDD() - 类 中的方法org.apache.spark.sql.Dataset
-
Returns the content of the Dataset as a JavaRDD of Ts.
- toJson(Matrix) - 类 中的静态方法org.apache.spark.ml.linalg.JsonMatrixConverter
-
Coverts the Matrix to a JSON string.
- toJson(Vector) - 类 中的静态方法org.apache.spark.ml.linalg.JsonVectorConverter
-
Coverts the vector to a JSON string.
- toJson() - 类 中的方法org.apache.spark.mllib.linalg.DenseVector
-
- toJson() - 类 中的方法org.apache.spark.mllib.linalg.SparseVector
-
- toJson() - 接口 中的方法org.apache.spark.mllib.linalg.Vector
-
Converts the vector to a JSON string.
- toJson() - 类 中的方法org.apache.spark.resource.ResourceInformation
-
- toJSON() - 类 中的方法org.apache.spark.sql.Dataset
-
Returns the content of the Dataset as a Dataset of JSON strings.
- toJValue() - 类 中的方法org.apache.spark.resource.ResourceInformationJson
-
- TOKEN_KIND() - 类 中的静态方法org.apache.spark.kafka010.KafkaTokenUtil
-
- Tokenizer - org.apache.spark.ml.feature中的类
-
A tokenizer that converts the input string to lowercase and then splits it by white spaces.
- Tokenizer(String) - 类 的构造器org.apache.spark.ml.feature.Tokenizer
-
- Tokenizer() - 类 的构造器org.apache.spark.ml.feature.Tokenizer
-
- tokens() - 类 中的方法org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.UpdateDelegationTokens
-
- tol() - 类 中的方法org.apache.spark.ml.classification.LinearSVC
-
- tol() - 类 中的方法org.apache.spark.ml.classification.LinearSVCModel
-
- tol() - 类 中的方法org.apache.spark.ml.classification.LogisticRegression
-
- tol() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- tol() - 类 中的方法org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-
- tol() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixture
-
- tol() - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- tol() - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- tol() - 类 中的方法org.apache.spark.ml.clustering.KMeansModel
-
- tol() - 接口 中的方法org.apache.spark.ml.param.shared.HasTol
-
Param for the convergence tolerance for iterative algorithms (>= 0).
- tol() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- tol() - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- tol() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegression
-
- tol() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
- tol() - 类 中的方法org.apache.spark.ml.regression.LinearRegression
-
- tol() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionModel
-
- toLocal() - 类 中的方法org.apache.spark.ml.clustering.DistributedLDAModel
-
Convert this distributed model to a local representation.
- toLocal() - 类 中的方法org.apache.spark.mllib.clustering.DistributedLDAModel
-
Convert model to a local model.
- toLocalIterator() - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Return an iterator that contains all of the elements in this RDD.
- toLocalIterator() - 类 中的方法org.apache.spark.rdd.RDD
-
Return an iterator that contains all of the elements in this RDD.
- toLocalIterator() - 类 中的方法org.apache.spark.sql.Dataset
-
Returns an iterator that contains all rows in this Dataset.
- toLocalMatrix() - 类 中的方法org.apache.spark.mllib.linalg.distributed.BlockMatrix
-
Collect the distributed matrix on the driver as a DenseMatrix.
- toLong(byte) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- toLong(Decimal) - 接口 中的方法org.apache.spark.sql.types.Decimal.DecimalIsConflicted
-
- toLong() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- toLong(Decimal) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- toLong(double) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- toLong(float) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- toLong(int) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- toLong(long) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- toLong(short) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- toLongArray() - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- toLowercase() - 类 中的方法org.apache.spark.ml.feature.RegexTokenizer
-
Indicates whether to convert all characters to lowercase before tokenizing.
- toMetadata(Metadata) - 类 中的方法org.apache.spark.ml.attribute.Attribute
-
Converts to ML metadata with some existing metadata.
- toMetadata() - 类 中的方法org.apache.spark.ml.attribute.Attribute
-
Converts to ML metadata
- toMetadata(Metadata) - 类 中的方法org.apache.spark.ml.attribute.AttributeGroup
-
Converts to ML metadata with some existing metadata.
- toMetadata() - 类 中的方法org.apache.spark.ml.attribute.AttributeGroup
-
Converts to ML metadata
- toMetadata(Metadata) - 类 中的静态方法org.apache.spark.ml.attribute.UnresolvedAttribute
-
- toMetadata() - 类 中的静态方法org.apache.spark.ml.attribute.UnresolvedAttribute
-
- toMicroBatchStream(String) - 接口 中的方法org.apache.spark.sql.connector.read.Scan
-
Returns the physical representation of this scan for streaming query with micro-batch mode.
- toNetty() - 接口 中的方法org.apache.spark.storage.BlockData
-
Returns a Netty-friendly wrapper for the block's data.
- toNetty() - 类 中的方法org.apache.spark.storage.DiskBlockData
-
Returns a Netty-friendly wrapper for the block's data.
- toNumber(String, Function1<String, T>, String, String) - 类 中的静态方法org.apache.spark.internal.config.ConfigHelpers
-
- toOld() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeModel
-
Convert to spark.mllib DecisionTreeModel (losing some information)
- toOld() - 接口 中的方法org.apache.spark.ml.tree.Split
-
Convert to old Split format
- tooltip(String, String) - 类 中的静态方法org.apache.spark.ui.UIUtils
-
- ToolTips - org.apache.spark.ui.storage中的类
-
- ToolTips() - 类 的构造器org.apache.spark.ui.storage.ToolTips
-
- ToolTips - org.apache.spark.ui中的类
-
- ToolTips() - 类 的构造器org.apache.spark.ui.ToolTips
-
- toOps(T, ClassTag<VD>) - 接口 中的方法org.apache.spark.graphx.impl.VertexPartitionBaseOpsConstructor
-
- top(int, Comparator<T>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Returns the top k (largest) elements from this RDD as defined by
the specified Comparator[T] and maintains the order.
- top(int) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Returns the top k (largest) elements from this RDD using the
natural ordering for T and maintains the order.
- top(int, Ordering<T>) - 类 中的方法org.apache.spark.rdd.RDD
-
Returns the top k (largest) elements from this RDD as defined by the specified
implicit Ordering[T] and maintains the ordering.
- toPairDStreamFunctions(DStream<Tuple2<K, V>>, ClassTag<K>, ClassTag<V>, Ordering<K>) - 类 中的静态方法org.apache.spark.streaming.dstream.DStream
-
- topByKey(int, Ordering<V>) - 类 中的方法org.apache.spark.mllib.rdd.MLPairRDDFunctions
-
Returns the top k (largest) elements for each key from this RDD as defined by the specified
implicit Ordering[T].
- topDocumentsPerTopic(int) - 类 中的方法org.apache.spark.mllib.clustering.DistributedLDAModel
-
Return the top documents for each topic
- topicAssignments() - 类 中的方法org.apache.spark.mllib.clustering.DistributedLDAModel
-
- topicConcentration() - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- topicConcentration() - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
- topicConcentration() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics'
distributions over terms.
- topicConcentration() - 类 中的方法org.apache.spark.mllib.clustering.DistributedLDAModel
-
- topicConcentration() - 类 中的方法org.apache.spark.mllib.clustering.LDAModel
-
Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics'
distributions over terms.
- topicConcentration() - 类 中的方法org.apache.spark.mllib.clustering.LocalLDAModel
-
- topicDistribution(Vector) - 类 中的方法org.apache.spark.mllib.clustering.LocalLDAModel
-
Predicts the topic mixture distribution for a document (often called "theta" in the
literature).
- topicDistributionCol() - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- topicDistributionCol() - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
- topicDistributionCol() - 接口 中的方法org.apache.spark.ml.clustering.LDAParams
-
Output column with estimates of the topic mixture distribution for each document (often called
"theta" in the literature).
- topicDistributions() - 类 中的方法org.apache.spark.mllib.clustering.DistributedLDAModel
-
For each document in the training set, return the distribution over topics for that document
("theta_doc").
- topicDistributions(RDD<Tuple2<Object, Vector>>) - 类 中的方法org.apache.spark.mllib.clustering.LocalLDAModel
-
Predicts the topic mixture distribution for each document (often called "theta" in the
literature).
- topicDistributions(JavaPairRDD<Long, Vector>) - 类 中的方法org.apache.spark.mllib.clustering.LocalLDAModel
-
Java-friendly version of topicDistributions
- topics() - 类 中的方法org.apache.spark.mllib.clustering.LocalLDAModel
-
- topicsMatrix() - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
Inferred topics, where each topic is represented by a distribution over terms.
- topicsMatrix() - 类 中的方法org.apache.spark.mllib.clustering.DistributedLDAModel
-
- topicsMatrix() - 类 中的方法org.apache.spark.mllib.clustering.LDAModel
-
Inferred topics, where each topic is represented by a distribution over terms.
- topicsMatrix() - 类 中的方法org.apache.spark.mllib.clustering.LocalLDAModel
-
- topK(Iterator<Tuple2<String, Object>>, int) - 类 中的静态方法org.apache.spark.streaming.util.RawTextHelper
-
Gets the top k words in terms of word counts.
- toPMML(StreamResult) - 接口 中的方法org.apache.spark.mllib.pmml.PMMLExportable
-
Export the model to the stream result in PMML format
- toPMML(String) - 接口 中的方法org.apache.spark.mllib.pmml.PMMLExportable
-
Export the model to a local file in PMML format
- toPMML(SparkContext, String) - 接口 中的方法org.apache.spark.mllib.pmml.PMMLExportable
-
Export the model to a directory on a distributed file system in PMML format
- toPMML(OutputStream) - 接口 中的方法org.apache.spark.mllib.pmml.PMMLExportable
-
Export the model to the OutputStream in PMML format
- toPMML() - 接口 中的方法org.apache.spark.mllib.pmml.PMMLExportable
-
Export the model to a String in PMML format
- topNode() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel
-
- Topology - org.apache.spark.ml.ann中的接口
-
Trait for the artificial neural network (ANN) topology properties
- topologyFile() - 类 中的方法org.apache.spark.storage.FileBasedTopologyMapper
-
- topologyInfo() - 类 中的方法org.apache.spark.storage.BlockManagerId
-
- topologyMap() - 类 中的方法org.apache.spark.storage.FileBasedTopologyMapper
-
- TopologyMapper - org.apache.spark.storage中的类
-
::DeveloperApi::
TopologyMapper provides topology information for a given host
param: conf SparkConf to get required properties, if needed
- TopologyMapper(SparkConf) - 类 的构造器org.apache.spark.storage.TopologyMapper
-
- TopologyModel - org.apache.spark.ml.ann中的接口
-
Trait for ANN topology model
- toPredict() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.PredictData
-
- topTopicsPerDocument(int) - 类 中的方法org.apache.spark.mllib.clustering.DistributedLDAModel
-
For each document, return the top k weighted topics for that document and their weights.
- toRDD(JavaDoubleRDD) - 类 中的静态方法org.apache.spark.api.java.JavaDoubleRDD
-
- toRDD(JavaPairRDD<K, V>) - 类 中的静态方法org.apache.spark.api.java.JavaPairRDD
-
- toRDD(JavaRDD<T>) - 类 中的静态方法org.apache.spark.api.java.JavaRDD
-
- toResourceInformation() - 类 中的方法org.apache.spark.resource.ResourceInformationJson
-
- toRowMatrix() - 类 中的方法org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
-
Converts to RowMatrix, dropping row indices after grouping by row index.
- toRowMatrix() - 类 中的方法org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix
-
Drops row indices and converts this matrix to a
RowMatrix.
- toScalaBigInt() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- toSeq() - 类 中的方法org.apache.spark.ml.param.ParamMap
-
Converts this param map to a sequence of param pairs.
- toSeq() - 接口 中的方法org.apache.spark.sql.Row
-
Return a Scala Seq representing the row.
- toShort() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- toShortArray() - 类 中的方法org.apache.spark.sql.vectorized.ColumnarArray
-
- toSparkContext(JavaSparkContext) - 类 中的静态方法org.apache.spark.api.java.JavaSparkContext
-
- toSparse() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Converts this matrix to a sparse matrix while maintaining the layout of the current matrix.
- toSparse() - 接口 中的方法org.apache.spark.ml.linalg.Vector
-
Converts this vector to a sparse vector with all explicit zeros removed.
- toSparse() - 类 中的方法org.apache.spark.mllib.linalg.DenseMatrix
-
Generate a SparseMatrix from the given DenseMatrix.
- toSparse() - 接口 中的方法org.apache.spark.mllib.linalg.Vector
-
Converts this vector to a sparse vector with all explicit zeros removed.
- toSparseColMajor() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Converts this matrix to a sparse matrix in column major order.
- toSparseMatrix(boolean) - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Converts this matrix to a sparse matrix.
- toSparseRowMajor() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Converts this matrix to a sparse matrix in row major order.
- toSparseWithSize(int) - 接口 中的方法org.apache.spark.ml.linalg.Vector
-
Converts this vector to a sparse vector with all explicit zeros removed when the size is known.
- toSparseWithSize(int) - 接口 中的方法org.apache.spark.mllib.linalg.Vector
-
Converts this vector to a sparse vector with all explicit zeros removed when the size is known.
- toSplit() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.SplitData
-
- toSplitInfo(Class<?>, String, InputSplit) - 类 中的静态方法org.apache.spark.scheduler.SplitInfo
-
- toSplitInfo(Class<?>, String, InputSplit) - 类 中的静态方法org.apache.spark.scheduler.SplitInfo
-
- toString() - 类 中的方法org.apache.spark.api.java.JavaRDD
-
- toString() - 类 中的方法org.apache.spark.api.java.Optional
-
- toString() - 类 中的方法org.apache.spark.broadcast.Broadcast
-
- toString() - 类 中的静态方法org.apache.spark.CleanAccum
-
- toString() - 类 中的静态方法org.apache.spark.CleanBroadcast
-
- toString() - 类 中的静态方法org.apache.spark.CleanCheckpoint
-
- toString() - 类 中的静态方法org.apache.spark.CleanRDD
-
- toString() - 类 中的静态方法org.apache.spark.CleanShuffle
-
- toString() - 类 中的方法org.apache.spark.ContextBarrierId
-
- toString() - 类 中的静态方法org.apache.spark.ExceptionFailure
-
- toString() - 类 中的静态方法org.apache.spark.ExecutorLostFailure
-
- toString() - 类 中的静态方法org.apache.spark.ExecutorRegistered
-
- toString() - 类 中的静态方法org.apache.spark.ExecutorRemoved
-
- toString() - 类 中的静态方法org.apache.spark.FetchFailed
-
- toString() - 类 中的方法org.apache.spark.graphx.EdgeDirection
-
- toString() - 类 中的方法org.apache.spark.graphx.EdgeTriplet
-
- toString() - 类 中的方法org.apache.spark.ml.attribute.Attribute
-
- toString() - 类 中的方法org.apache.spark.ml.attribute.AttributeGroup
-
- toString() - 类 中的静态方法org.apache.spark.ml.attribute.UnresolvedAttribute
-
- toString() - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- toString() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- toString() - 类 中的方法org.apache.spark.ml.classification.LogisticRegressionModel
-
- toString() - 类 中的方法org.apache.spark.ml.classification.NaiveBayesModel
-
- toString() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- toString() - 类 中的静态方法org.apache.spark.ml.clustering.ClusterData
-
- toString() - 类 中的方法org.apache.spark.ml.feature.LabeledPoint
-
- toString() - 类 中的方法org.apache.spark.ml.feature.RFormula
-
- toString() - 类 中的方法org.apache.spark.ml.feature.RFormulaModel
-
- toString() - 类 中的方法org.apache.spark.ml.linalg.DenseVector
-
- toString() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
A human readable representation of the matrix
- toString(int, int) - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
A human readable representation of the matrix with maximum lines and width
- toString() - 类 中的方法org.apache.spark.ml.linalg.SparseVector
-
- toString() - 类 中的方法org.apache.spark.ml.param.Param
-
- toString() - 类 中的方法org.apache.spark.ml.param.ParamMap
-
- toString() - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- toString() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- toString() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionTrainingSummary
-
- toString() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- toString() - 类 中的静态方法org.apache.spark.ml.SaveInstanceEnd
-
- toString() - 类 中的静态方法org.apache.spark.ml.SaveInstanceStart
-
- toString() - 类 中的静态方法org.apache.spark.ml.TransformEnd
-
- toString() - 类 中的静态方法org.apache.spark.ml.TransformStart
-
- toString() - 接口 中的方法org.apache.spark.ml.tree.DecisionTreeModel
-
Summary of the model
- toString() - 类 中的方法org.apache.spark.ml.tree.InternalNode
-
- toString() - 类 中的方法org.apache.spark.ml.tree.LeafNode
-
- toString() - 接口 中的方法org.apache.spark.ml.tree.TreeEnsembleModel
-
Summary of the model
- toString() - 接口 中的方法org.apache.spark.ml.util.Identifiable
-
- toString() - 类 中的静态方法org.apache.spark.mllib.classification.impl.GLMClassificationModel.SaveLoadV1_0$.Data
-
- toString() - 类 中的方法org.apache.spark.mllib.classification.LogisticRegressionModel
-
- toString() - 类 中的静态方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV1_0$.Data
-
- toString() - 类 中的静态方法org.apache.spark.mllib.classification.NaiveBayesModel.SaveLoadV2_0$.Data
-
- toString() - 类 中的方法org.apache.spark.mllib.classification.SVMModel
-
- toString() - 类 中的静态方法org.apache.spark.mllib.feature.ChiSqSelectorModel.SaveLoadV1_0$.Data
-
- toString() - 类 中的静态方法org.apache.spark.mllib.feature.VocabWord
-
- toString() - 类 中的方法org.apache.spark.mllib.fpm.AssociationRules.Rule
-
- toString() - 类 中的方法org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
-
- toString() - 类 中的方法org.apache.spark.mllib.linalg.DenseVector
-
- toString() - 类 中的静态方法org.apache.spark.mllib.linalg.distributed.IndexedRow
-
- toString() - 类 中的静态方法org.apache.spark.mllib.linalg.distributed.MatrixEntry
-
- toString() - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
A human readable representation of the matrix
- toString(int, int) - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
A human readable representation of the matrix with maximum lines and width
- toString() - 类 中的方法org.apache.spark.mllib.linalg.SparseVector
-
- toString() - 类 中的静态方法org.apache.spark.mllib.recommendation.Rating
-
- toString() - 类 中的方法org.apache.spark.mllib.regression.GeneralizedLinearModel
-
Print a summary of the model.
- toString() - 类 中的静态方法org.apache.spark.mllib.regression.impl.GLMRegressionModel.SaveLoadV1_0$.Data
-
- toString() - 类 中的方法org.apache.spark.mllib.regression.LabeledPoint
-
- toString() - 类 中的方法org.apache.spark.mllib.stat.test.BinarySample
-
- toString() - 类 中的方法org.apache.spark.mllib.stat.test.ChiSqTestResult
-
- toString() - 类 中的方法org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult
-
- toString() - 接口 中的方法org.apache.spark.mllib.stat.test.TestResult
-
String explaining the hypothesis test result.
- toString() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.Algo
-
- toString() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.EnsembleCombiningStrategy
-
- toString() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.FeatureType
-
- toString() - 类 中的静态方法org.apache.spark.mllib.tree.configuration.QuantileStrategy
-
- toString() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel
-
Print a summary of the model.
- toString() - 类 中的方法org.apache.spark.mllib.tree.model.InformationGainStats
-
- toString() - 类 中的方法org.apache.spark.mllib.tree.model.Node
-
- toString() - 类 中的方法org.apache.spark.mllib.tree.model.Predict
-
- toString() - 类 中的方法org.apache.spark.mllib.tree.model.Split
-
- toString() - 类 中的方法org.apache.spark.partial.BoundedDouble
-
- toString() - 类 中的方法org.apache.spark.partial.PartialResult
-
- toString() - 类 中的静态方法org.apache.spark.rdd.CheckpointState
-
- toString() - 类 中的静态方法org.apache.spark.rdd.DeterministicLevel
-
- toString() - 类 中的方法org.apache.spark.rdd.RDD
-
- toString() - 类 中的方法org.apache.spark.resource.ResourceInformation
-
- toString() - 类 中的静态方法org.apache.spark.resource.ResourceInformationJson
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.AccumulableInfo
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.AskPermissionToCommitOutput
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.BlacklistedExecutor
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.ExecutorKilled
-
- toString() - 类 中的方法org.apache.spark.scheduler.InputFormatInfo
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.local.KillTask
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.local.ReviveOffers
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.local.StatusUpdate
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.local.StopExecutor
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.LossReasonPending
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SchedulingMode
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerApplicationEnd
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerApplicationStart
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerBlockManagerAdded
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerBlockManagerRemoved
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerBlockUpdated
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerEnvironmentUpdate
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerExecutorAdded
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerExecutorBlacklisted
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerExecutorBlacklistedForStage
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerExecutorMetricsUpdate
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerExecutorRemoved
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerExecutorUnblacklisted
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerJobEnd
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerJobStart
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerLogStart
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerNodeBlacklisted
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerNodeBlacklistedForStage
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerNodeUnblacklisted
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerSpeculativeTaskSubmitted
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerStageCompleted
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerStageExecutorMetrics
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerStageSubmitted
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerTaskEnd
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerTaskGettingResult
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerTaskStart
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.SparkListenerUnpersistRDD
-
- toString() - 类 中的方法org.apache.spark.scheduler.SplitInfo
-
- toString() - 类 中的静态方法org.apache.spark.scheduler.TaskLocality
-
- toString() - 类 中的方法org.apache.spark.SerializableWritable
-
- toString() - 类 中的方法org.apache.spark.sql.catalog.Column
-
- toString() - 类 中的方法org.apache.spark.sql.catalog.Database
-
- toString() - 类 中的方法org.apache.spark.sql.catalog.Function
-
- toString() - 类 中的方法org.apache.spark.sql.catalog.Table
-
- toString() - 类 中的方法org.apache.spark.sql.Column
-
- toString() - 类 中的方法org.apache.spark.sql.connector.read.streaming.Offset
-
- toString() - 类 中的方法org.apache.spark.sql.Dataset
-
- toString() - 类 中的静态方法org.apache.spark.sql.dynamicpruning.PlanDynamicPruningFilters
-
- toString() - 类 中的静态方法org.apache.spark.sql.hive.execution.CreateHiveTableAsSelectCommand
-
- toString() - 类 中的静态方法org.apache.spark.sql.hive.execution.InsertIntoHiveDirCommand
-
- toString() - 类 中的静态方法org.apache.spark.sql.hive.execution.InsertIntoHiveTable
-
- toString() - 类 中的静态方法org.apache.spark.sql.hive.execution.OptimizedCreateHiveTableAsSelectCommand
-
- toString() - 类 中的静态方法org.apache.spark.sql.hive.execution.ScriptTransformationExec
-
- toString() - 类 中的静态方法org.apache.spark.sql.hive.HiveUDAFBuffer
-
- toString() - 类 中的方法org.apache.spark.sql.hive.orc.OrcFileFormat
-
- toString() - 类 中的静态方法org.apache.spark.sql.hive.RelationConversions
-
- toString() - 类 中的静态方法org.apache.spark.sql.jdbc.JdbcType
-
- toString() - 类 中的方法org.apache.spark.sql.KeyValueGroupedDataset
-
- toString() - 接口 中的方法org.apache.spark.sql.RelationalGroupedDataset.GroupType
-
- toString() - 类 中的方法org.apache.spark.sql.RelationalGroupedDataset
-
- toString() - 接口 中的方法org.apache.spark.sql.Row
-
- toString() - 类 中的静态方法org.apache.spark.sql.sources.AlwaysFalse
-
- toString() - 类 中的静态方法org.apache.spark.sql.sources.AlwaysTrue
-
- toString() - 类 中的静态方法org.apache.spark.sql.sources.And
-
- toString() - 类 中的静态方法org.apache.spark.sql.sources.EqualNullSafe
-
- toString() - 类 中的静态方法org.apache.spark.sql.sources.EqualTo
-
- toString() - 类 中的静态方法org.apache.spark.sql.sources.GreaterThan
-
- toString() - 类 中的静态方法org.apache.spark.sql.sources.GreaterThanOrEqual
-
- toString() - 类 中的方法org.apache.spark.sql.sources.In
-
- toString() - 类 中的静态方法org.apache.spark.sql.sources.IsNotNull
-
- toString() - 类 中的静态方法org.apache.spark.sql.sources.IsNull
-
- toString() - 类 中的静态方法org.apache.spark.sql.sources.LessThan
-
- toString() - 类 中的静态方法org.apache.spark.sql.sources.LessThanOrEqual
-
- toString() - 类 中的静态方法org.apache.spark.sql.sources.Not
-
- toString() - 类 中的静态方法org.apache.spark.sql.sources.Or
-
- toString() - 类 中的静态方法org.apache.spark.sql.sources.StringContains
-
- toString() - 类 中的静态方法org.apache.spark.sql.sources.StringEndsWith
-
- toString() - 类 中的静态方法org.apache.spark.sql.sources.StringStartsWith
-
- toString() - 类 中的方法org.apache.spark.sql.streaming.SinkProgress
-
- toString() - 类 中的方法org.apache.spark.sql.streaming.SourceProgress
-
- toString() - 类 中的方法org.apache.spark.sql.streaming.StateOperatorProgress
-
- toString() - 异常错误 中的方法org.apache.spark.sql.streaming.StreamingQueryException
-
- toString() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryProgress
-
- toString() - 类 中的方法org.apache.spark.sql.streaming.StreamingQueryStatus
-
- toString() - 类 中的静态方法org.apache.spark.sql.types.CharType
-
- toString() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- toString() - 类 中的方法org.apache.spark.sql.types.DecimalType
-
- toString() - 类 中的方法org.apache.spark.sql.types.Metadata
-
- toString() - 类 中的方法org.apache.spark.sql.types.StructField
-
- toString() - 类 中的静态方法org.apache.spark.sql.types.VarcharType
-
- toString() - 类 中的静态方法org.apache.spark.status.api.v1.ApplicationAttemptInfo
-
- toString() - 类 中的静态方法org.apache.spark.status.api.v1.ApplicationInfo
-
- toString() - 类 中的方法org.apache.spark.status.api.v1.StackTrace
-
- toString() - 类 中的静态方法org.apache.spark.status.api.v1.ThreadStackTrace
-
- toString() - 类 中的方法org.apache.spark.storage.BlockId
-
- toString() - 类 中的方法org.apache.spark.storage.BlockManagerId
-
- toString() - 类 中的静态方法org.apache.spark.storage.BroadcastBlockId
-
- toString() - 类 中的静态方法org.apache.spark.storage.RDDBlockId
-
- toString() - 类 中的方法org.apache.spark.storage.RDDInfo
-
- toString() - 类 中的静态方法org.apache.spark.storage.ShuffleBlockBatchId
-
- toString() - 类 中的静态方法org.apache.spark.storage.ShuffleBlockId
-
- toString() - 类 中的静态方法org.apache.spark.storage.ShuffleDataBlockId
-
- toString() - 类 中的静态方法org.apache.spark.storage.ShuffleIndexBlockId
-
- toString() - 类 中的方法org.apache.spark.storage.StorageLevel
-
- toString() - 类 中的静态方法org.apache.spark.storage.StreamBlockId
-
- toString() - 类 中的静态方法org.apache.spark.storage.TaskResultBlockId
-
- toString() - 类 中的方法org.apache.spark.streaming.Duration
-
- toString() - 类 中的静态方法org.apache.spark.streaming.scheduler.BatchInfo
-
- toString() - 类 中的静态方法org.apache.spark.streaming.scheduler.OutputOperationInfo
-
- toString() - 类 中的静态方法org.apache.spark.streaming.scheduler.ReceiverInfo
-
- toString() - 类 中的静态方法org.apache.spark.streaming.scheduler.ReceiverState
-
- toString() - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerBatchCompleted
-
- toString() - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerBatchStarted
-
- toString() - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerBatchSubmitted
-
- toString() - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerOutputOperationCompleted
-
- toString() - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerOutputOperationStarted
-
- toString() - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerReceiverError
-
- toString() - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerReceiverStarted
-
- toString() - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerReceiverStopped
-
- toString() - 类 中的静态方法org.apache.spark.streaming.scheduler.StreamingListenerStreamingStarted
-
- toString() - 类 中的方法org.apache.spark.streaming.State
-
- toString() - 类 中的方法org.apache.spark.streaming.Time
-
- toString() - 类 中的静态方法org.apache.spark.TaskCommitDenied
-
- toString() - 类 中的静态方法org.apache.spark.TaskKilled
-
- toString() - 类 中的静态方法org.apache.spark.TaskState
-
- toString() - 类 中的方法org.apache.spark.util.AccumulatorV2
-
- toString() - 类 中的方法org.apache.spark.util.MutablePair
-
- toString() - 类 中的方法org.apache.spark.util.StatCounter
-
- toStructField(Metadata) - 类 中的方法org.apache.spark.ml.attribute.Attribute
-
Converts to a StructField with some existing metadata.
- toStructField() - 类 中的方法org.apache.spark.ml.attribute.Attribute
-
Converts to a StructField.
- toStructField(Metadata) - 类 中的方法org.apache.spark.ml.attribute.AttributeGroup
-
Converts to a StructField with some existing metadata.
- toStructField() - 类 中的方法org.apache.spark.ml.attribute.AttributeGroup
-
Converts to a StructField.
- toStructField(Metadata) - 类 中的静态方法org.apache.spark.ml.attribute.UnresolvedAttribute
-
- toStructField() - 类 中的静态方法org.apache.spark.ml.attribute.UnresolvedAttribute
-
- totalBlocksFetched() - 类 中的方法org.apache.spark.status.api.v1.ShuffleReadMetricDistributions
-
- totalBytesRead(ShuffleReadMetrics) - 类 中的静态方法org.apache.spark.ui.jobs.ApiHelper
-
- totalCores() - 类 中的方法org.apache.spark.scheduler.cluster.ExecutorInfo
-
- totalCores() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- totalCores() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- totalCount() - 类 中的方法org.apache.spark.util.sketch.CountMinSketch
-
- totalDelay() - 类 中的方法org.apache.spark.status.api.v1.streaming.BatchInfo
-
- totalDelay() - 类 中的方法org.apache.spark.streaming.scheduler.BatchInfo
-
Time taken for all the jobs of this batch to finish processing from the time they
were submitted.
- totalDiskSize() - 类 中的方法org.apache.spark.ui.storage.ExecutorStreamSummary
-
- totalDuration() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- totalDuration() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- totalGCTime() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- totalGcTime() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- totalInputBytes() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- totalInputBytes() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- totalIterations() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionTrainingSummary
-
Number of training iterations.
- totalIterations() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionTrainingSummary
-
Number of training iterations until termination
This value is only available when using the "l-bfgs" solver.
- totalMemSize() - 类 中的方法org.apache.spark.ui.storage.ExecutorStreamSummary
-
- totalNumNodes() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- totalNumNodes() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- totalNumNodes() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- totalNumNodes() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- totalNumNodes() - 接口 中的方法org.apache.spark.ml.tree.TreeEnsembleModel
-
Total number of nodes, summed over all trees in the ensemble.
- totalOffHeap() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- totalOffHeapStorageMemory() - 接口 中的方法org.apache.spark.SparkExecutorInfo
-
- totalOffHeapStorageMemory() - 类 中的方法org.apache.spark.SparkExecutorInfoImpl
-
- totalOffHeapStorageMemory() - 类 中的方法org.apache.spark.status.api.v1.MemoryMetrics
-
- totalOnHeap() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- totalOnHeapStorageMemory() - 接口 中的方法org.apache.spark.SparkExecutorInfo
-
- totalOnHeapStorageMemory() - 类 中的方法org.apache.spark.SparkExecutorInfoImpl
-
- totalOnHeapStorageMemory() - 类 中的方法org.apache.spark.status.api.v1.MemoryMetrics
-
- totalShuffleRead() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- totalShuffleRead() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- totalShuffleWrite() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- totalShuffleWrite() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- totalTasks() - 类 中的方法org.apache.spark.status.api.v1.ExecutorSummary
-
- totalTasks() - 类 中的方法org.apache.spark.status.LiveExecutor
-
- toTuple() - 类 中的方法org.apache.spark.graphx.EdgeTriplet
-
- toTypeInfo() - 类 中的方法org.apache.spark.sql.hive.HiveInspectors.typeInfoConversions
-
- toUnscaledLong() - 类 中的方法org.apache.spark.sql.types.Decimal
-
- toVirtualHosts(Seq<String>) - 类 中的静态方法org.apache.spark.ui.JettyUtils
-
- train(RDD<ALS.Rating<ID>>, int, int, int, int, double, boolean, double, boolean, StorageLevel, StorageLevel, int, long, ClassTag<ID>, Ordering<ID>) - 类 中的静态方法org.apache.spark.ml.recommendation.ALS
-
:: DeveloperApi ::
Implementation of the ALS algorithm.
- train(RDD<LabeledPoint>) - 类 中的静态方法org.apache.spark.mllib.classification.NaiveBayes
-
Trains a Naive Bayes model given an RDD of (label, features) pairs.
- train(RDD<LabeledPoint>, double) - 类 中的静态方法org.apache.spark.mllib.classification.NaiveBayes
-
Trains a Naive Bayes model given an RDD of (label, features) pairs.
- train(RDD<LabeledPoint>, double, String) - 类 中的静态方法org.apache.spark.mllib.classification.NaiveBayes
-
Trains a Naive Bayes model given an RDD of (label, features) pairs.
- train(RDD<LabeledPoint>, int, double, double, double, Vector) - 类 中的静态方法org.apache.spark.mllib.classification.SVMWithSGD
-
Train a SVM model given an RDD of (label, features) pairs.
- train(RDD<LabeledPoint>, int, double, double, double) - 类 中的静态方法org.apache.spark.mllib.classification.SVMWithSGD
-
Train a SVM model given an RDD of (label, features) pairs.
- train(RDD<LabeledPoint>, int, double, double) - 类 中的静态方法org.apache.spark.mllib.classification.SVMWithSGD
-
Train a SVM model given an RDD of (label, features) pairs.
- train(RDD<LabeledPoint>, int) - 类 中的静态方法org.apache.spark.mllib.classification.SVMWithSGD
-
Train a SVM model given an RDD of (label, features) pairs.
- train(RDD<Vector>, int, int, String, long) - 类 中的静态方法org.apache.spark.mllib.clustering.KMeans
-
Trains a k-means model using the given set of parameters.
- train(RDD<Vector>, int, int, String) - 类 中的静态方法org.apache.spark.mllib.clustering.KMeans
-
Trains a k-means model using the given set of parameters.
- train(RDD<Vector>, int, int) - 类 中的静态方法org.apache.spark.mllib.clustering.KMeans
-
Trains a k-means model using specified parameters and the default values for unspecified.
- train(RDD<Rating>, int, int, double, int, long) - 类 中的静态方法org.apache.spark.mllib.recommendation.ALS
-
Train a matrix factorization model given an RDD of ratings by users for a subset of products.
- train(RDD<Rating>, int, int, double, int) - 类 中的静态方法org.apache.spark.mllib.recommendation.ALS
-
Train a matrix factorization model given an RDD of ratings by users for a subset of products.
- train(RDD<Rating>, int, int, double) - 类 中的静态方法org.apache.spark.mllib.recommendation.ALS
-
Train a matrix factorization model given an RDD of ratings by users for a subset of products.
- train(RDD<Rating>, int, int) - 类 中的静态方法org.apache.spark.mllib.recommendation.ALS
-
Train a matrix factorization model given an RDD of ratings by users for a subset of products.
- train(RDD<LabeledPoint>, Strategy) - 类 中的静态方法org.apache.spark.mllib.tree.DecisionTree
-
Method to train a decision tree model.
- train(RDD<LabeledPoint>, Enumeration.Value, Impurity, int) - 类 中的静态方法org.apache.spark.mllib.tree.DecisionTree
-
Method to train a decision tree model.
- train(RDD<LabeledPoint>, Enumeration.Value, Impurity, int, int) - 类 中的静态方法org.apache.spark.mllib.tree.DecisionTree
-
Method to train a decision tree model.
- train(RDD<LabeledPoint>, Enumeration.Value, Impurity, int, int, int, Enumeration.Value, Map<Object, Object>) - 类 中的静态方法org.apache.spark.mllib.tree.DecisionTree
-
Method to train a decision tree model.
- train(RDD<LabeledPoint>, BoostingStrategy) - 类 中的静态方法org.apache.spark.mllib.tree.GradientBoostedTrees
-
Method to train a gradient boosting model.
- train(JavaRDD<LabeledPoint>, BoostingStrategy) - 类 中的静态方法org.apache.spark.mllib.tree.GradientBoostedTrees
-
Java-friendly API for org.apache.spark.mllib.tree.GradientBoostedTrees.train
- trainClassifier(RDD<LabeledPoint>, int, Map<Object, Object>, String, int, int) - 类 中的静态方法org.apache.spark.mllib.tree.DecisionTree
-
Method to train a decision tree model for binary or multiclass classification.
- trainClassifier(JavaRDD<LabeledPoint>, int, Map<Integer, Integer>, String, int, int) - 类 中的静态方法org.apache.spark.mllib.tree.DecisionTree
-
Java-friendly API for org.apache.spark.mllib.tree.DecisionTree.trainClassifier
- trainClassifier(RDD<LabeledPoint>, Strategy, int, String, int) - 类 中的静态方法org.apache.spark.mllib.tree.RandomForest
-
Method to train a decision tree model for binary or multiclass classification.
- trainClassifier(RDD<LabeledPoint>, int, Map<Object, Object>, int, String, String, int, int, int) - 类 中的静态方法org.apache.spark.mllib.tree.RandomForest
-
Method to train a decision tree model for binary or multiclass classification.
- trainClassifier(JavaRDD<LabeledPoint>, int, Map<Integer, Integer>, int, String, String, int, int, int) - 类 中的静态方法org.apache.spark.mllib.tree.RandomForest
-
Java-friendly API for org.apache.spark.mllib.tree.RandomForest.trainClassifier
- trainImplicit(RDD<Rating>, int, int, double, int, double, long) - 类 中的静态方法org.apache.spark.mllib.recommendation.ALS
-
Train a matrix factorization model given an RDD of 'implicit preferences' given by users
to some products, in the form of (userID, productID, preference) pairs.
- trainImplicit(RDD<Rating>, int, int, double, int, double) - 类 中的静态方法org.apache.spark.mllib.recommendation.ALS
-
Train a matrix factorization model given an RDD of 'implicit preferences' of users for a
subset of products.
- trainImplicit(RDD<Rating>, int, int, double, double) - 类 中的静态方法org.apache.spark.mllib.recommendation.ALS
-
Train a matrix factorization model given an RDD of 'implicit preferences' of users for a
subset of products.
- trainImplicit(RDD<Rating>, int, int) - 类 中的静态方法org.apache.spark.mllib.recommendation.ALS
-
Train a matrix factorization model given an RDD of 'implicit preferences' of users for a
subset of products.
- trainingCost() - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeansSummary
-
- trainingCost() - 类 中的方法org.apache.spark.ml.clustering.KMeansSummary
-
- trainingCost() - 类 中的方法org.apache.spark.mllib.clustering.BisectingKMeansModel
-
- trainingCost() - 类 中的方法org.apache.spark.mllib.clustering.KMeansModel
-
- trainingLogLikelihood() - 类 中的方法org.apache.spark.ml.clustering.DistributedLDAModel
-
- trainingSummary() - 接口 中的方法org.apache.spark.ml.util.HasTrainingSummary
-
- trainOn(DStream<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.StreamingKMeans
-
Update the clustering model by training on batches of data from a DStream.
- trainOn(JavaDStream<Vector>) - 类 中的方法org.apache.spark.mllib.clustering.StreamingKMeans
-
Java-friendly version of trainOn.
- trainOn(DStream<LabeledPoint>) - 类 中的方法org.apache.spark.mllib.regression.StreamingLinearAlgorithm
-
Update the model by training on batches of data from a DStream.
- trainOn(JavaDStream<LabeledPoint>) - 类 中的方法org.apache.spark.mllib.regression.StreamingLinearAlgorithm
-
Java-friendly version of trainOn.
- trainRatio() - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplit
-
- trainRatio() - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplitModel
-
- trainRatio() - 接口 中的方法org.apache.spark.ml.tuning.TrainValidationSplitParams
-
Param for ratio between train and validation data.
- trainRegressor(RDD<LabeledPoint>, Map<Object, Object>, String, int, int) - 类 中的静态方法org.apache.spark.mllib.tree.DecisionTree
-
Method to train a decision tree model for regression.
- trainRegressor(JavaRDD<LabeledPoint>, Map<Integer, Integer>, String, int, int) - 类 中的静态方法org.apache.spark.mllib.tree.DecisionTree
-
Java-friendly API for org.apache.spark.mllib.tree.DecisionTree.trainRegressor
- trainRegressor(RDD<LabeledPoint>, Strategy, int, String, int) - 类 中的静态方法org.apache.spark.mllib.tree.RandomForest
-
Method to train a decision tree model for regression.
- trainRegressor(RDD<LabeledPoint>, Map<Object, Object>, int, String, String, int, int, int) - 类 中的静态方法org.apache.spark.mllib.tree.RandomForest
-
Method to train a decision tree model for regression.
- trainRegressor(JavaRDD<LabeledPoint>, Map<Integer, Integer>, int, String, String, int, int, int) - 类 中的静态方法org.apache.spark.mllib.tree.RandomForest
-
Java-friendly API for org.apache.spark.mllib.tree.RandomForest.trainRegressor
- TrainValidationSplit - org.apache.spark.ml.tuning中的类
-
Validation for hyper-parameter tuning.
- TrainValidationSplit(String) - 类 的构造器org.apache.spark.ml.tuning.TrainValidationSplit
-
- TrainValidationSplit() - 类 的构造器org.apache.spark.ml.tuning.TrainValidationSplit
-
- TrainValidationSplitModel - org.apache.spark.ml.tuning中的类
-
Model from train validation split.
- TrainValidationSplitModel.TrainValidationSplitModelWriter - org.apache.spark.ml.tuning中的类
-
Writer for TrainValidationSplitModel.
- TrainValidationSplitParams - org.apache.spark.ml.tuning中的接口
-
- transferMapSpillFile(File, long[]) - 接口 中的方法org.apache.spark.shuffle.api.SingleSpillShuffleMapOutputWriter
-
Transfer a file that contains the bytes of all the partitions written by this map task.
- transferred() - 类 中的方法org.apache.spark.storage.ReadableChannelFileRegion
-
- transferTo(WritableByteChannel, long) - 类 中的方法org.apache.spark.storage.ReadableChannelFileRegion
-
- transform(Function1<Try<T>, Try<S>>, ExecutionContext) - 类 中的方法org.apache.spark.ComplexFutureAction
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.classification.ClassificationModel
-
Transforms dataset by reading from featuresCol, and appending new columns as specified by
parameters:
- predicted labels as predictionCol of type Double
- raw predictions (confidences) as rawPredictionCol of type Vector.
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.classification.DecisionTreeClassificationModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.classification.OneVsRestModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.classification.ProbabilisticClassificationModel
-
Transforms dataset by reading from featuresCol, and appending new columns as specified by
parameters:
- predicted labels as predictionCol of type Double
- raw predictions (confidences) as rawPredictionCol of type Vector
- probability of each class as probabilityCol of type Vector.
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.clustering.KMeansModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
Transforms the input dataset.
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.Binarizer
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.Bucketizer
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelectorModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.ColumnPruner
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.CountVectorizerModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.FeatureHasher
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.HashingTF
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.IDFModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.ImputerModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.IndexToString
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.Interaction
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.MaxAbsScalerModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.MinMaxScalerModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.OneHotEncoderModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.PCAModel
-
Transform a vector by computed Principal Components.
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.RFormulaModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.RobustScalerModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.SQLTransformer
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.StandardScalerModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.StopWordsRemover
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.StringIndexerModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.VectorAssembler
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.VectorAttributeRewriter
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.VectorIndexerModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.VectorSizeHint
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.VectorSlicer
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.feature.Word2VecModel
-
Transform a sentence column to a vector column to represent the whole sentence.
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.fpm.FPGrowthModel
-
The transform method first generates the association rules according to the frequent itemsets.
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.PipelineModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.PredictionModel
-
Transforms dataset by reading from featuresCol, calling predict, and storing
the predictions as a new column predictionCol.
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.recommendation.ALSModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.regression.DecisionTreeRegressionModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.regression.IsotonicRegressionModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- transform(Dataset<?>, ParamPair<?>, ParamPair<?>...) - 类 中的方法org.apache.spark.ml.Transformer
-
Transforms the dataset with optional parameters
- transform(Dataset<?>, ParamPair<?>, Seq<ParamPair<?>>) - 类 中的方法org.apache.spark.ml.Transformer
-
Transforms the dataset with optional parameters
- transform(Dataset<?>, ParamMap) - 类 中的方法org.apache.spark.ml.Transformer
-
Transforms the dataset with provided parameter map as additional parameters.
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.Transformer
-
Transforms the input dataset.
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.tuning.CrossValidatorModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplitModel
-
- transform(Dataset<?>) - 类 中的方法org.apache.spark.ml.UnaryTransformer
-
- transform(Vector) - 类 中的方法org.apache.spark.mllib.feature.ChiSqSelectorModel
-
Applies transformation on a vector.
- transform(Vector) - 类 中的方法org.apache.spark.mllib.feature.ElementwiseProduct
-
Does the hadamard product transformation.
- transform(Iterable<?>) - 类 中的方法org.apache.spark.mllib.feature.HashingTF
-
Transforms the input document into a sparse term frequency vector.
- transform(Iterable<?>) - 类 中的方法org.apache.spark.mllib.feature.HashingTF
-
Transforms the input document into a sparse term frequency vector (Java version).
- transform(RDD<D>) - 类 中的方法org.apache.spark.mllib.feature.HashingTF
-
Transforms the input document to term frequency vectors.
- transform(JavaRDD<D>) - 类 中的方法org.apache.spark.mllib.feature.HashingTF
-
Transforms the input document to term frequency vectors (Java version).
- transform(RDD<Vector>) - 类 中的方法org.apache.spark.mllib.feature.IDFModel
-
Transforms term frequency (TF) vectors to TF-IDF vectors.
- transform(Vector) - 类 中的方法org.apache.spark.mllib.feature.IDFModel
-
Transforms a term frequency (TF) vector to a TF-IDF vector
- transform(JavaRDD<Vector>) - 类 中的方法org.apache.spark.mllib.feature.IDFModel
-
Transforms term frequency (TF) vectors to TF-IDF vectors (Java version).
- transform(Vector) - 类 中的方法org.apache.spark.mllib.feature.Normalizer
-
Applies unit length normalization on a vector.
- transform(Vector) - 类 中的方法org.apache.spark.mllib.feature.PCAModel
-
Transform a vector by computed Principal Components.
- transform(Vector) - 类 中的方法org.apache.spark.mllib.feature.StandardScalerModel
-
Applies standardization transformation on a vector.
- transform(Vector) - 接口 中的方法org.apache.spark.mllib.feature.VectorTransformer
-
Applies transformation on a vector.
- transform(RDD<Vector>) - 接口 中的方法org.apache.spark.mllib.feature.VectorTransformer
-
Applies transformation on an RDD[Vector].
- transform(JavaRDD<Vector>) - 接口 中的方法org.apache.spark.mllib.feature.VectorTransformer
-
Applies transformation on a JavaRDD[Vector].
- transform(String) - 类 中的方法org.apache.spark.mllib.feature.Word2VecModel
-
Transforms a word to its vector representation
- transform(Function1<Try<T>, Try<S>>, ExecutionContext) - 类 中的方法org.apache.spark.SimpleFutureAction
-
- Transform - org.apache.spark.sql.connector.expressions中的接口
-
Represents a transform function in the public logical expression API.
- transform(Function1<Dataset<T>, Dataset<U>>) - 类 中的方法org.apache.spark.sql.Dataset
-
Concise syntax for chaining custom transformations.
- transform(Column, Function1<Column, Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns an array of elements after applying a tranformation to each element
in the input array.
- transform(Column, Function2<Column, Column, Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns an array of elements after applying a tranformation to each element
in the input array.
- transform(Function<R, JavaRDD<U>>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD is generated by applying a function
on each RDD of 'this' DStream.
- transform(Function2<R, Time, JavaRDD<U>>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD is generated by applying a function
on each RDD of 'this' DStream.
- transform(List<JavaDStream<?>>, Function2<List<JavaRDD<?>>, Time, JavaRDD<T>>) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Create a new DStream in which each RDD is generated by applying a function on RDDs of
the DStreams.
- transform(Function1<RDD<T>, RDD<U>>, ClassTag<U>) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream in which each RDD is generated by applying a function
on each RDD of 'this' DStream.
- transform(Function2<RDD<T>, Time, RDD<U>>, ClassTag<U>) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream in which each RDD is generated by applying a function
on each RDD of 'this' DStream.
- transform(Seq<DStream<?>>, Function2<Seq<RDD<?>>, Time, RDD<T>>, ClassTag<T>) - 类 中的方法org.apache.spark.streaming.StreamingContext
-
Create a new DStream in which each RDD is generated by applying a function on RDDs of
the DStreams.
- transform_keys(Column, Function2<Column, Column, Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Applies a function to every key-value pair in a map and returns
a map with the results of those applications as the new keys for the pairs.
- transform_values(Column, Function2<Column, Column, Column>) - 类 中的静态方法org.apache.spark.sql.functions
-
Applies a function to every key-value pair in a map and returns
a map with the results of those applications as the new values for the pairs.
- TransformEnd - org.apache.spark.ml中的类
-
Event fired after Transformer.transform.
- TransformEnd() - 类 的构造器org.apache.spark.ml.TransformEnd
-
- transformer() - 类 中的方法org.apache.spark.ml.TransformEnd
-
- Transformer - org.apache.spark.ml中的类
-
:: DeveloperApi ::
Abstract class for transformers that transform one dataset into another.
- Transformer() - 类 的构造器org.apache.spark.ml.Transformer
-
- transformer() - 类 中的方法org.apache.spark.ml.TransformStart
-
- TransformHelper(Seq<Transform>) - 类 的构造器org.apache.spark.sql.connector.catalog.CatalogV2Implicits.TransformHelper
-
- transformImpl(Dataset<?>) - 类 中的方法org.apache.spark.ml.classification.ClassificationModel
-
- transformOutputColumnSchema(StructField, String, boolean, boolean) - 类 中的静态方法org.apache.spark.ml.feature.OneHotEncoderCommon
-
Prepares the StructField with proper metadata for OneHotEncoder's output column.
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.classification.OneVsRest
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.classification.OneVsRestModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeans
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.clustering.BisectingKMeansModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixture
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.clustering.GaussianMixtureModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.clustering.KMeans
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.clustering.KMeansModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.clustering.LDA
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.clustering.LDAModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.Binarizer
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.BucketedRandomProjectionLSH
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.Bucketizer
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelector
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.ChiSqSelectorModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.ColumnPruner
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.CountVectorizer
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.CountVectorizerModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.FeatureHasher
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.HashingTF
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.IDF
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.IDFModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.Imputer
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.ImputerModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.IndexToString
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.Interaction
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.MaxAbsScaler
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.MaxAbsScalerModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.MinHashLSH
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.MinMaxScaler
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.MinMaxScalerModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.OneHotEncoder
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.OneHotEncoderModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.PCA
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.PCAModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.QuantileDiscretizer
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.RFormula
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.RFormulaModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.RobustScaler
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.RobustScalerModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.SQLTransformer
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.StandardScaler
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.StandardScalerModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.StopWordsRemover
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.StringIndexer
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.StringIndexerModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.VectorAssembler
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.VectorAttributeRewriter
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.VectorIndexer
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.VectorIndexerModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.VectorSizeHint
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.VectorSlicer
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.Word2Vec
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.feature.Word2VecModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.fpm.FPGrowth
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.fpm.FPGrowthModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.Pipeline
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.PipelineModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.PipelineStage
-
:: DeveloperApi ::
Check transform validity and derive the output schema from the input schema.
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.PredictionModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.Predictor
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.recommendation.ALS
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.recommendation.ALSModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegression
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.regression.AFTSurvivalRegressionModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.regression.IsotonicRegression
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.regression.IsotonicRegressionModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.tuning.CrossValidator
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.tuning.CrossValidatorModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplit
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.tuning.TrainValidationSplitModel
-
- transformSchema(StructType) - 类 中的方法org.apache.spark.ml.UnaryTransformer
-
- transformSchemaImpl(StructType) - 接口 中的方法org.apache.spark.ml.tuning.ValidatorParams
-
- TransformStart - org.apache.spark.ml中的类
-
Event fired before Transformer.transform.
- TransformStart() - 类 的构造器org.apache.spark.ml.TransformStart
-
- transformToPair(Function<R, JavaPairRDD<K2, V2>>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD is generated by applying a function
on each RDD of 'this' DStream.
- transformToPair(Function2<R, Time, JavaPairRDD<K2, V2>>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD is generated by applying a function
on each RDD of 'this' DStream.
- transformToPair(List<JavaDStream<?>>, Function2<List<JavaRDD<?>>, Time, JavaPairRDD<K, V>>) - 类 中的方法org.apache.spark.streaming.api.java.JavaStreamingContext
-
Create a new DStream in which each RDD is generated by applying a function on RDDs of
the DStreams.
- transformWith(Function1<Try<T>, Future<S>>, ExecutionContext) - 类 中的方法org.apache.spark.ComplexFutureAction
-
- transformWith(Function1<Try<T>, Future<S>>, ExecutionContext) - 类 中的方法org.apache.spark.SimpleFutureAction
-
- transformWith(JavaDStream<U>, Function3<R, JavaRDD<U>, Time, JavaRDD<W>>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD is generated by applying a function
on each RDD of 'this' DStream and 'other' DStream.
- transformWith(JavaPairDStream<K2, V2>, Function3<R, JavaPairRDD<K2, V2>, Time, JavaRDD<W>>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD is generated by applying a function
on each RDD of 'this' DStream and 'other' DStream.
- transformWith(DStream<U>, Function2<RDD<T>, RDD<U>, RDD<V>>, ClassTag<U>, ClassTag<V>) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream in which each RDD is generated by applying a function
on each RDD of 'this' DStream and 'other' DStream.
- transformWith(DStream<U>, Function3<RDD<T>, RDD<U>, Time, RDD<V>>, ClassTag<U>, ClassTag<V>) - 类 中的方法org.apache.spark.streaming.dstream.DStream
-
Return a new DStream in which each RDD is generated by applying a function
on each RDD of 'this' DStream and 'other' DStream.
- transformWithToPair(JavaDStream<U>, Function3<R, JavaRDD<U>, Time, JavaPairRDD<K2, V2>>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD is generated by applying a function
on each RDD of 'this' DStream and 'other' DStream.
- transformWithToPair(JavaPairDStream<K2, V2>, Function3<R, JavaPairRDD<K2, V2>, Time, JavaPairRDD<K3, V3>>) - 接口 中的方法org.apache.spark.streaming.api.java.JavaDStreamLike
-
Return a new DStream in which each RDD is generated by applying a function
on each RDD of 'this' DStream and 'other' DStream.
- translate(Column, String, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Translate any character in the src by a character in replaceString.
- transpose() - 类 中的方法org.apache.spark.ml.linalg.DenseMatrix
-
- transpose() - 接口 中的方法org.apache.spark.ml.linalg.Matrix
-
Transpose the Matrix.
- transpose() - 类 中的方法org.apache.spark.ml.linalg.SparseMatrix
-
- transpose() - 类 中的方法org.apache.spark.mllib.linalg.DenseMatrix
-
- transpose() - 类 中的方法org.apache.spark.mllib.linalg.distributed.BlockMatrix
-
Transpose this BlockMatrix.
- transpose() - 类 中的方法org.apache.spark.mllib.linalg.distributed.CoordinateMatrix
-
Transposes this CoordinateMatrix.
- transpose() - 接口 中的方法org.apache.spark.mllib.linalg.Matrix
-
Transpose the Matrix.
- transpose() - 类 中的方法org.apache.spark.mllib.linalg.SparseMatrix
-
- treeAggregate(U, Function2<U, T, U>, Function2<U, U, U>, int) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Aggregates the elements of this RDD in a multi-level tree pattern.
- treeAggregate(U, Function2<U, T, U>, Function2<U, U, U>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
org.apache.spark.api.java.JavaRDDLike.treeAggregate with suggested depth 2.
- treeAggregate(U, Function2<U, T, U>, Function2<U, U, U>, int, ClassTag<U>) - 类 中的方法org.apache.spark.rdd.RDD
-
Aggregates the elements of this RDD in a multi-level tree pattern.
- TreeClassifierParams - org.apache.spark.ml.tree中的接口
-
Parameters for Decision Tree-based classification algorithms.
- TreeEnsembleClassifierParams - org.apache.spark.ml.tree中的接口
-
Parameters for Decision Tree-based ensemble classification algorithms.
- TreeEnsembleModel<M extends DecisionTreeModel> - org.apache.spark.ml.tree中的接口
-
Abstraction for models which are ensembles of decision trees
- TreeEnsembleParams - org.apache.spark.ml.tree中的接口
-
Parameters for Decision Tree-based ensemble algorithms.
- TreeEnsembleRegressorParams - org.apache.spark.ml.tree中的接口
-
Parameters for Decision Tree-based ensemble regression algorithms.
- treeID() - 类 中的方法org.apache.spark.ml.tree.EnsembleModelReadWrite.EnsembleNodeData
-
- treeId() - 类 中的方法org.apache.spark.mllib.tree.model.DecisionTreeModel.SaveLoadV1_0$.NodeData
-
- treeReduce(Function2<T, T, T>, int) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
Reduces the elements of this RDD in a multi-level tree pattern.
- treeReduce(Function2<T, T, T>) - 接口 中的方法org.apache.spark.api.java.JavaRDDLike
-
org.apache.spark.api.java.JavaRDDLike.treeReduce with suggested depth 2.
- treeReduce(Function2<T, T, T>, int) - 类 中的方法org.apache.spark.rdd.RDD
-
Reduces the elements of this RDD in a multi-level tree pattern.
- TreeRegressorParams - org.apache.spark.ml.tree中的接口
-
Parameters for Decision Tree-based regression algorithms.
- trees() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- trees() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- trees() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- trees() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- trees() - 接口 中的方法org.apache.spark.ml.tree.TreeEnsembleModel
-
Trees in this ensemble.
- trees() - 类 中的方法org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
-
- trees() - 类 中的方法org.apache.spark.mllib.tree.model.RandomForestModel
-
- treeStrategy() - 类 中的方法org.apache.spark.mllib.tree.configuration.BoostingStrategy
-
- treeString() - 类 中的方法org.apache.spark.sql.types.StructType
-
- treeString(int) - 类 中的方法org.apache.spark.sql.types.StructType
-
- treeWeights() - 类 中的方法org.apache.spark.ml.classification.GBTClassificationModel
-
- treeWeights() - 类 中的方法org.apache.spark.ml.classification.RandomForestClassificationModel
-
- treeWeights() - 类 中的方法org.apache.spark.ml.regression.GBTRegressionModel
-
- treeWeights() - 类 中的方法org.apache.spark.ml.regression.RandomForestRegressionModel
-
- treeWeights() - 接口 中的方法org.apache.spark.ml.tree.TreeEnsembleModel
-
Weights for each tree, zippable with trees
- treeWeights() - 类 中的方法org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
-
- triangleCount() - 类 中的方法org.apache.spark.graphx.GraphOps
-
Compute the number of triangles passing through each vertex.
- TriangleCount - org.apache.spark.graphx.lib中的类
-
Compute the number of triangles passing through each vertex.
- TriangleCount() - 类 的构造器org.apache.spark.graphx.lib.TriangleCount
-
- trigger(Trigger) - 类 中的方法org.apache.spark.sql.streaming.DataStreamWriter
-
Set the trigger for the stream query.
- Trigger - org.apache.spark.sql.streaming中的类
-
Policy used to indicate how often results should be produced by a [[StreamingQuery]].
- Trigger() - 类 的构造器org.apache.spark.sql.streaming.Trigger
-
- TriggerThreadDump$() - 类 的构造器org.apache.spark.storage.BlockManagerMessages.TriggerThreadDump$
-
- trim(Column) - 类 中的静态方法org.apache.spark.sql.functions
-
Trim the spaces from both ends for the specified string column.
- trim(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Trim the specified character from both ends for the specified string column.
- TrimHorizon() - 类 的构造器org.apache.spark.streaming.kinesis.KinesisInitialPositions.TrimHorizon
-
- TripletFields - org.apache.spark.graphx中的类
-
Represents a subset of the fields of an [[EdgeTriplet]] or [[EdgeContext]].
- TripletFields() - 类 的构造器org.apache.spark.graphx.TripletFields
-
Constructs a default TripletFields in which all fields are included.
- TripletFields(boolean, boolean, boolean) - 类 的构造器org.apache.spark.graphx.TripletFields
-
- triplets() - 类 中的方法org.apache.spark.graphx.Graph
-
An RDD containing the edge triplets, which are edges along with the vertex data associated with
the adjacent vertices.
- triplets() - 类 中的方法org.apache.spark.graphx.impl.GraphImpl
-
- truePositiveRate(double) - 类 中的方法org.apache.spark.mllib.evaluation.MulticlassMetrics
-
Returns true positive rate for a given label (category)
- truePositiveRateByLabel() - 接口 中的方法org.apache.spark.ml.classification.LogisticRegressionSummary
-
Returns true positive rate for each label (category).
- trunc(Column, String) - 类 中的静态方法org.apache.spark.sql.functions
-
Returns date truncated to the unit specified by the format.
- truncate() - 接口 中的方法org.apache.spark.sql.connector.write.SupportsOverwrite
-
- truncate() - 接口 中的方法org.apache.spark.sql.connector.write.SupportsTruncate
-
Configures a write to replace all existing data with data committed in the write.
- tryCompare(T, T) - 类 中的静态方法org.apache.spark.sql.types.ByteExactNumeric
-
- tryCompare(T, T) - 类 中的静态方法org.apache.spark.sql.types.DecimalExactNumeric
-
- tryCompare(T, T) - 类 中的静态方法org.apache.spark.sql.types.DoubleExactNumeric
-
- tryCompare(T, T) - 类 中的静态方法org.apache.spark.sql.types.FloatExactNumeric
-
- tryCompare(T, T) - 类 中的静态方法org.apache.spark.sql.types.IntegerExactNumeric
-
- tryCompare(T, T) - 类 中的静态方法org.apache.spark.sql.types.LongExactNumeric
-
- tryCompare(T, T) - 类 中的静态方法org.apache.spark.sql.types.ShortExactNumeric
-
- tryLog(Function0<T>) - 类 中的静态方法org.apache.spark.util.Utils
-
Executes the given block in a Try, logging any uncaught exceptions.
- tryLogNonFatalError(Function0<BoxedUnit>) - 类 中的静态方法org.apache.spark.util.Utils
-
Executes the given block.
- tryOrExit(Function0<BoxedUnit>) - 类 中的静态方法org.apache.spark.util.Utils
-
Execute a block of code that evaluates to Unit, forwarding any uncaught exceptions to the
default UncaughtExceptionHandler
NOTE: This method is to be called by the spark-started JVM process.
- tryOrIOException(Function0<T>) - 类 中的静态方法org.apache.spark.util.Utils
-
Execute a block of code that returns a value, re-throwing any non-fatal uncaught
exceptions as IOException.
- tryOrStopSparkContext(SparkContext, Function0<BoxedUnit>) - 类 中的静态方法org.apache.spark.util.Utils
-
Execute a block of code that evaluates to Unit, stop SparkContext if there is any uncaught
exception
NOTE: This method is to be called by the driver-side components to avoid stopping the
user-started JVM process completely; in contrast, tryOrExit is to be called in the
spark-started JVM process .
- tryRecoverFromCheckpoint(String) - 类 中的方法org.apache.spark.streaming.StreamingContextPythonHelper
-
This is a private method only for Python to implement getOrCreate.
- tryWithResource(Function0<R>, Function1<R, T>) - 类 中的静态方法org.apache.spark.util.Utils
-
- tryWithSafeFinally(Function0<T>, Function0<BoxedUnit>) - 类 中的静态方法org.apache.spark.util.Utils
-
Execute a block of code, then a finally block, but if exceptions happen in
the finally block, do not suppress the original exception.
- tryWithSafeFinallyAndFailureCallbacks(Function0<T>, Function0<BoxedUnit>, Function0<BoxedUnit>) - 类 中的静态方法org.apache.spark.util.Utils
-
Execute a block of code and call the failure callbacks in the catch block.
- tuple(Encoder<T1>, Encoder<T2>) - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for 2-ary tuples.
- tuple(Encoder<T1>, Encoder<T2>, Encoder<T3>) - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for 3-ary tuples.
- tuple(Encoder<T1>, Encoder<T2>, Encoder<T3>, Encoder<T4>) - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for 4-ary tuples.
- tuple(Encoder<T1>, Encoder<T2>, Encoder<T3>, Encoder<T4>, Encoder<T5>) - 类 中的静态方法org.apache.spark.sql.Encoders
-
An encoder for 5-ary tuples.
- tValues() - 类 中的方法org.apache.spark.ml.regression.GeneralizedLinearRegressionTrainingSummary
-
- tValues() - 类 中的方法org.apache.spark.ml.regression.LinearRegressionSummary
-
- Tweedie$() - 类 的构造器org.apache.spark.ml.regression.GeneralizedLinearRegression.Tweedie$
-
- TYPE() - 类 中的静态方法org.apache.spark.ml.attribute.AttributeKeys
-
- typed - org.apache.spark.sql.expressions.javalang中的类
-
- typed() - 类 的构造器org.apache.spark.sql.expressions.javalang.typed
-
已过时。
- typed - org.apache.spark.sql.expressions.scalalang中的类
-
- typed() - 类 的构造器org.apache.spark.sql.expressions.scalalang.typed
-
已过时。
- TypedColumn<T,U> - org.apache.spark.sql中的类
-
A
Column where an
Encoder has been given for the expected input and return type.
- TypedColumn(Expression, ExpressionEncoder<U>) - 类 的构造器org.apache.spark.sql.TypedColumn
-
- typedLit(T, TypeTags.TypeTag<T>) - 类 中的静态方法org.apache.spark.sql.functions
-
Creates a
Column of literal value.
- typeInfoConversions(DataType) - 类 的构造器org.apache.spark.sql.hive.HiveInspectors.typeInfoConversions
-
- typeInfoConversions(DataType) - 类 中的静态方法org.apache.spark.sql.hive.orc.OrcFileFormat
-
- typeName() - 类 中的方法org.apache.spark.mllib.linalg.VectorUDT
-
- typeName() - 类 中的静态方法org.apache.spark.sql.types.BinaryType
-
- typeName() - 类 中的静态方法org.apache.spark.sql.types.BooleanType
-
- typeName() - 类 中的静态方法org.apache.spark.sql.types.ByteType
-
- typeName() - 类 中的静态方法org.apache.spark.sql.types.CalendarIntervalType
-
- typeName() - 类 中的方法org.apache.spark.sql.types.DataType
-
Name of the type used in JSON serialization.
- typeName() - 类 中的静态方法org.apache.spark.sql.types.DateType
-
- typeName() - 类 中的方法org.apache.spark.sql.types.DecimalType
-
- typeName() - 类 中的静态方法org.apache.spark.sql.types.DoubleType
-
- typeName() - 类 中的静态方法org.apache.spark.sql.types.FloatType
-
- typeName() - 类 中的静态方法org.apache.spark.sql.types.IntegerType
-
- typeName() - 类 中的静态方法org.apache.spark.sql.types.LongType
-
- typeName() - 类 中的静态方法org.apache.spark.sql.types.NullType
-
- typeName() - 类 中的静态方法org.apache.spark.sql.types.ShortType
-
- typeName() - 类 中的静态方法org.apache.spark.sql.types.StringType
-
- typeName() - 类 中的静态方法org.apache.spark.sql.types.TimestampType
-